CALVIN COLLEGE ENGINEERING
Cellulosic to Gasoline Final Report
Team 2: Dynamic Organics
Kaylea Brase, Aimee Diepstra, Mitchell Groenenboom, Philip Reinken
Calvin College
Department of Engineering
Engineering 340
May 8, 2013
© Calvin College Engineering and Kaylea Brase, Aimee Diepstra, Mitchell Groenenboom, Philip Reinken
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Executive Summary
Senior Design Team “Dynamic Organics” is comprised of four chemical engineering students: Kaylea
Brase, Aimee Diepstra, Mitchell Groenenboom, and Philip Reinken. For our project, we performed an indepth feasibility study on a process which converts cellulosic material into a mixture of gasoline blending
components.
In today’s society, which is becoming ever-more reliant on the petroleum industry, research into
alternative solutions is becoming a greater priority every day. In addition to increasing petroleum prices,
the push towards conserving valuable resources is becoming a driving force behind engineering design.
The balances between the social, environmental, and economic impacts of this process have affected
many key decisions of the design. The primary social impact taken into consideration was the
identification of a feedstock that was not also considered a food source. Currently, E85 ethanol, a corn
derivative, is a special fuel which is being used in some engines. This use of corn has affected the market
price of corn, which has limited access to lower-income demographics. The environmental impact is twofold. First, by seeking an alternative method for gasoline production, we are diverting our dependence on
fossil fuels to other means. Second, as we are carefully monitoring the waste and product streams
throughout the process, we are considering how to properly dispose of any environmental hazards that
may result.
We investigated two potential solutions: a small scale solution involving the processing of food scraps
and large scale solutions for the conversion of cellulosic material into a useable fuel source. In our
analysis, it was determined that the most efficient process would be a catalytic conversion of the
cellulosic material to fuel via synthesis gas. As a result of this analysis, the small-scale conversion of food
waste was set aside in order to focus energy on the large-scale process.
This process design consists of five main stages: a solid feed-handling system, a gasification unit, a
purification system designed to remove sulfur components from synthesis gas, a production unit
consisting of two reactors, and a final separation unit. The plan was designed and optimized in UNISIM
or Polymath.
The resulting facility is capable of producing 125.7 million gallons (10,930 Bbl/day) of gasoline
components annually, using 17 square miles of cropland. The plant could produce about 0.09% of the 134
billion gallons of gasoline consumed in the United States. After performing a cash flow analysis, we
calculated an internal rate of return of 11.72% with a payback period of 6 years when our gasoline is sold
at $3.67/gallon, comparable to premium gas prices. Accounting for feedstock costs, utilities, sulfur credit,
and transportation costs, variable costs total to $1.86/gallon of gasoline while operating costs total to
$1.38/gallon of gasoline.
Through economic analysis, the process was determined to be economically feasible at the current time.
While the technology surrounding the product reactor which produces gasoline is still in its infancy, there
is still great potential to learn from the research and optimization of this system.
The following report highlights the research, design, optimization, and economic requirements
surrounding this facility and its overall feasibility.
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Table of Contents
Executive Summary....................................................................................................................................... ii
Table of Figures ........................................................................................................................................... vii
Table of Tables ............................................................................................................................................. ix
1.
Introduction .......................................................................................................................................... 1
1.1 “Dynamic Organics” ............................................................................................................................ 1
1.1.1 Need ............................................................................................................................................. 1
1.1.2 Approach ...................................................................................................................................... 2
1.1.3 Benefit .......................................................................................................................................... 2
1.1.4 Similar Case Studies ..................................................................................................................... 3
1.1.5 Design Goals ................................................................................................................................. 4
1.2
Project Scope ................................................................................................................................ 5
1.3
Project Team Management .......................................................................................................... 5
1.3.1 Introduction to Our Team ............................................................................................................ 5
1.3.2 Team Duties ................................................................................................................................. 6
1.3.3 Schedule Management .................................................................................................................... 7
2.
Decisions of Process and Feedstock ..................................................................................................... 7
2.1 Overall Process .................................................................................................................................... 7
2.1.1 Enzymatic Approach .................................................................................................................... 7
2.1.2 Synthesis Gas Approach ............................................................................................................... 9
2.1.3 Process Decision......................................................................................................................... 11
2.2
Process Feedstock ....................................................................................................................... 12
2.2.1 Switchgrass................................................................................................................................. 12
2.2.2 Miscanthus Giganteus ................................................................................................................ 13
2.2.3 Fast Growing Poplar ................................................................................................................... 16
2.2.4 Cornstalks ................................................................................................................................... 17
2.2.5 Feedstock Decision..................................................................................................................... 18
2.3
3.
Other Design Notes ..................................................................................................................... 19
Feed Handling System ......................................................................................................................... 20
3.1 Site Determination and Equipment .................................................................................................. 20
3.2 On-Site Facilities and Equipment ...................................................................................................... 25
4.
Gasification ......................................................................................................................................... 33
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4.2 Options for Gasification Processes ................................................................................................... 33
4.3 Process Selection .............................................................................................................................. 36
4.4 Modeling in Aspen Plus ..................................................................................................................... 39
4.5 Adapted UNISIM modeling ............................................................................................................... 39
4.6.1 Pyrolysis ......................................................................................................................................... 40
4.6.2 Secondary Reactions ...................................................................................................................... 41
4.7 Char Oxidation and Purity ................................................................................................................. 43
4.8 Purification of flue gas ...................................................................................................................... 44
4.9 Optimization of process/Agreement with Reality ............................................................................ 48
4.10 Gasification Control Loops .............................................................................................................. 49
5.
Synthesis Gas Purification Unit ........................................................................................................... 49
5.1
Screening Criteria ........................................................................................................................ 49
5.2 Purification Alternatives ................................................................................................................... 51
5.2.1 The Rectisol Process ................................................................................................................... 51
5.2.2 The Selexol Process .................................................................................................................... 52
5.2.3 Amine Processes ........................................................................................................................ 53
5.2.4 The Hybrid Processes ................................................................................................................. 54
5.3
Purification Process Selection ..................................................................................................... 55
5.4 Rectisol Process Description and Simulation .................................................................................... 57
5.5 Optimization ..................................................................................................................................... 60
5.6 Vessel Specifications ......................................................................................................................... 63
5.7 Control and Safety ............................................................................................................................ 63
6.
Production Reactor System................................................................................................................. 66
6.1 Selection of a Product ....................................................................................................................... 68
6.2
Process Alternatives .................................................................................................................... 70
6.2.1 Fischer-Tropsch Synthesis .......................................................................................................... 71
6.2.2 Dimethyl Ether Synthesis ........................................................................................................... 72
6.2.3 Gasoline Products via Methanol ................................................................................................ 72
6.2.4 Process Selection ....................................................................................................................... 73
6.3 Synthesis Gas to Methanol Reactor .................................................................................................. 73
6.3.1 Modeling in an isothermal PBR .................................................................................................. 75
6.3.2 Modeling in a Non-isothermal Packed Shell and Tube Reactor................................................. 78
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6.3.3 Modeling in an Adjusted Non-Isothermal PBR .......................................................................... 80
6.3.4 Cu/ZnO-Al2O3 Catalyst ................................................................................................................ 84
6.3.5 Modeling of a Flash Column and Recycle .................................................................................. 85
6.3.6 Materials of Construction .......................................................................................................... 85
6.4 Vessel Specifications ......................................................................................................................... 86
6.5 Control and Safety ............................................................................................................................ 87
7.
Methanol to Gasoline Reactor ............................................................................................................ 87
7.1 Rate Law Determination ................................................................................................................... 89
7.2 Product Correlations ......................................................................................................................... 91
7.3 Reaction Enthalpy and Heat Capacity Correlations .......................................................................... 93
7.4 Polymath Modeling ........................................................................................................................... 94
7.5 Polymath Optimization ..................................................................................................................... 95
7.6 Optimization Goals............................................................................................................................ 98
7.7 Optimization Results: ...................................................................................................................... 100
7.8 Methanol to Gasoline Control Loops: ............................................................................................ 102
8.
Final Separation System .................................................................................................................... 103
9. UNISIM Model Integration .................................................................................................................... 110
10. Cost Analysis ....................................................................................................................................... 110
10.1 Chemical Engineering Index .......................................................................................................... 110
10.2 Equipment Cost ............................................................................................................................. 110
10.3 Total Capital Investmentg the sub-areas of the facility ................................................................ 112
10.4 Variable Costs................................................................................................................................ 114
10.5 Operating Cost .............................................................................................................................. 114
10.6 Return on Investment ................................................................................................................... 116
11. Conclusion ........................................................................................................................................... 116
12. References .......................................................................................................................................... 118
13. Acknowledgements............................................................................................................................. 125
14. Appendices .......................................................................................................................................... 126
Appendix A: Scheduling Details for the Cellulosics to Gasoline Project ............................................... 127
A.1 General Deadlines....................................................................................................................... 127
A.2 Task List....................................................................................................................................... 128
A.3 Project Gantt Chart ..................................................................................................................... 130
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Appendix B: Gantt Chart for Each Semester ......................................................................................... 132
Appendix C: Methanol to Gasoline Kinetic Model in Polymath............................................................ 134
Appendix D: Equipment Cost Calculation Sheets ................................................................................. 137
Appendix E: Cash Flows* ...................................................................................................................... 142
Appendix F: Equipment Specification Sheets ....................................................................................... 143
Appendix G: Octane Number Calculations ........................................................................................... 179
Appendix H: UNISIM Workbook for Syngas Purification ...................................................................... 180
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Table of Figures
Figure 1. Team members from left to right ................................................................................................... 5
Figure 2. Block Flow Diagram of the Enzymatic Process ............................................................................ 8
Figure 3. Syngas Process ............................................................................................................................ 10
Figure 4. Switchgrass growth in the United States. .................................................................................... 13
Figure 5. Approximate growing location for Miscanthus Giganteus. ......................................................... 15
Figure 6. Distribution of poplar species in the USA and Canada. .............................................................. 16
Figure 7. Corn Production Regions in the United States. ........................................................................... 17
Figure 8. An example of the size of a prospective area for a refinery from CropScape ............................. 22
Figure 9. A storage bunker with a truck unloading into an elevator conveyor system ............................... 26
Figure 10. A small elevator belt system that would be used to transport solids ........................................ 27
Figure 11. A feed silo used in the coal industry to control the feed to the furnaces ................................... 28
Figure 12. A gravity feed hopper used for a cattle feed system .................................................................. 29
Figure 13. Solid Feed Eductor System for a Pneumatic Feed Design ........................................................ 30
Figure 14. A screw conveyor feed system made by Wolf Industries.......................................................... 31
Figure 15. Schematic of Feed Handling Process. ....................................................................................... 32
Figure 16. A countercurrent fixed bed Gasification unit ........................................................................... 34
Figure 17. A fluidized bed reactor for the gasification of biomass ............................................................. 35
Figure 18. Operation of a dual-fluidized bed reactor .................................................................................. 36
Figure 19. A simplified model for the TNEE gasification process ............................................................. 38
Figure 20. The original model of TNEE technology in Aspen, from the lab of L. Abdelouahed. .............. 39
Figure 21. The operation of a wet scrubber purifying a dirty gas ............................................................... 45
Figure 22. The major components of the Wet Sulfuric Acid process ......................................................... 46
Figure 23 – Major components of the SNOX process ................................................................................. 47
Figure 24: Synthesis gas production ratios ................................................................................................. 48
Figure 25. Typical Rectisol Process Flow Scheme ..................................................................................... 52
Figure 26. Typical Selexol Process Flow Scheme ...................................................................................... 53
Figure 27. Typical Amine Process Flow Diagram ...................................................................................... 54
Figure 28. Typical Hybrid Process ............................................................................................................. 55
Figure 29. Rectisol Process Flow Diagram ................................................................................................. 58
Figure 30. Claus Sulfur Recovery Flow Scheme ........................................................................................ 59
Figure 31. Optimizing pressure in the absorber with an input of 2 lbmol/hr of fresh methanol ................. 60
Figure 32. Effect of fresh methanol flow in the absorber ........................................................................... 61
Figure 33. Optimizing number of trays in the absorber .............................................................................. 61
Figure 34. Effect of Feed Gas Temperature on H2S Removal ................................................................... 62
Figure 35. %Removal of H2S Required to achieve desired purity ............................................................. 63
Figure 36. Control of Hot Regenerator; typical tower control .................................................................... 65
Figure 37. Manipulation of Cold Stream By-pass ...................................................................................... 66
Figure 38. Production System Schematic as a PFD. ................................................................................... 67
Figure 39. Reaction Rates plotted in an isothermal reactor to determine optimum length. ........................ 76
Figure 40. Verification of an optimal reactor diameter for the MeOH production reactor. ........................ 76
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Figure 41. Optimizing Isothermal Reactor Temperature. ........................................................................... 77
Figure 42. Pressure Optimization in an isothermal MeOH reactor. ............................................................ 78
Figure 43. Non-isothermal MeOH Reactor Length Optimization. ............................................................. 79
Figure 44. Methanol yield in a non-isothermal reactor, varying inlet temperature .................................... 80
Figure 45. Determining reverse reaction rate constants for the methanol synthesis reactor. .................. 81
Figure 46. Determining optimal sizing constraints in an adjusted non-isothermal reactor......................... 82
Figure 47. Temperature profiles in an adjusted non-isothermal reactor. .................................................... 82
Figure 48. Effect of pressure in an adjusted non-isothermal reactor. ......................................................... 83
Figure 49. Inlet temperature optimization for an adjusted non-isothermal reactor. .................................... 84
Figure 50: Results assuming a first order rate law (left)
Figure 51: Results of a second order rate law
(right) .......................................................................................................................................................... 89
Figure 52: Results of a 1.5th order rate law (left) Figure 53: Results of a 1.75th order rate law (right) . 90
Figure 54: wt% Benzene compared to conversion (left)
Figure 55: wt% C10H14 compared to
conversion (right) ........................................................................................................................................ 92
Figure 56: Theoretical model for calculating the enthalpy of reaction at any temperature ........................ 93
Figure 57: Temperature profiles of the temperature effect simulations ...................................................... 96
Figure 58: Effect of Temperature on total conversion and production rate of desired products ................ 96
Figure 59: Relationship between reactor length and methanol conversion ................................................ 97
Figure 60: Relationship between the length of the reactor and the desired product production rate .......... 98
Figure 61: Illustrates the effect of initial coolant temperature and coolant flow rate ................................. 99
Figure 62: Desired product formation rate with respect to number of tubes ............................................ 100
Figure 63: Amount of product fuels lost to the vapor phase at various temperatures............................... 104
Figure 64: Amount of product fuels produced at various temperatures for optimization ......................... 105
Figure 65: Water content of our product fuels exiting the separator at various temperatures .................. 106
Figure 66: Schematic of Final Separations Process .................................................................................. 109
viii
Table of Tables
Table 1. Decision matrix for determining a process route. ........................................................................ 12
Table 2. Nominal annual yields of biomass crops ...................................................................................... 13
Table 3. Composition of Miscanthus Giganteus, presented by Michel,Gruber, et. al. ............................... 15
Table 4. Selected Composition and Production Data for Possible Feedstocks ........................................... 18
Table 5. Decision Matrix for Feedstock Selection. ..................................................................................... 18
Table 6. Table of acreage and land usage within the sample area in Figure 8 ............................................ 23
Table 7. Comparison of available dual fluidized bed reactor models ......................................................... 37
Table 8. Parameters for the correlation of Pyrolysis .................................................................................. 40
Table 9. Secondary kinetic reactions used to modify the pyrolysis product. ............................................. 42
Table 10: Elemental composition of char varying with temperature of formation ..................................... 43
Table 11. The effect of char yield and enthalpy of combustion on sand temperature ................................ 44
Table 12. Emission limits for coal burning power plant ............................................................................. 45
Table 13. The set of conditions assumed from the ASPEN model ............................................................ 48
Table 14. Purification Process Decision Matrix ......................................................................................... 56
Table 15. Properties of Fuel-Grade Alcohols and Gasoline ....................................................................... 68
Table 16: Costs per mile (2005) for E85 (Ethanolic Gasoline), Unleaded Gasoline and Butanol .............. 69
Table 17: Azeotropic Data for common Fuel Alcohols with Water, b.p.=100⁰C ....................................... 70
Table 18. Fischer-Tropsch Primary Reactions. ........................................................................................... 71
Table 19. Methanol Synthesis Frequency Factors for Kinetic Equations. .................................................. 74
Table 20. Materials of Construction Comparison for Synthesis Gas to Methanol Reactor. ....................... 86
Table 21: The methanol to gasoline data, showing the relative product yields at various temperatures .... 88
Table 22: Parameters to be used with composition correlations and their R^2 values ............................... 92
Table 23: Product flow rates and molar compositions .............................................................................. 102
Table 24: Freezing point of methanol-water binary mixtures by mass percent ........................................ 105
Table 25. Total Equipment Capital Cost for Cellulosic Material to Gasoline Facility. ............................ 111
Table 26. Total Equipment Capital Cost by Plant Area. ........................................................................... 112
Table 27. Total Capital Investment for the Cellulosic Material to Gasoline Facility. .............................. 113
Table 28. Variable Operating Costs of a Cellulosic to Gasoline Facility. ..... Error! Bookmark not defined.
Table 29. Fixed Operating Costs of a Cellulosic to Gasoline Facility ...................................................... 115
Table 30. Return on Investment Results ................................................................................................... 116
Table A.31. Task List................................................................................................................................ 130
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1. Introduction
1.1 “Dynamic Organics”
1.1.1 Need
According to the U.S. Energy Information Administration (EIA), “the transportation sector accounted for
27 percent of total world delivered energy consumption in 2008, and transportation energy use is
projected to increase by 1.4 percent per year from 2008 to 20351”. About half of the world’s supplied
liquid fuel is consumed by the transport sector, and this proportion is expected to rise, so attempting to
reduce the demand significantly is unrealistic. Development of new fuels or sources of energy often
require new types of engines and new systems of fuel transport and infrastructure, with the exception of
some biofuels made via complicated and expensive processes. Other than recent movements towards the
use of biodiesel in air transport, the aeronautics industry has very few alternatives to engines that operate
on traditional fuel sources. Since these strides in the production of aviation biofuel have occurred, some
airlines have experimented with the use of these alternate fuel sources in their commercial flights, but
traditional fuel sources are still the primary selection2. Because of the continued dependency on high
energy density liquid fuels, the transport sector will continue to rely mainly on crude oil imports.
However, a product that is consistent with the products on the marketplace but can be produced
sustainably on a domestic level would be an attractive solution.
A constant and affordable supply of fuel is needed for many reasons, including:
i.
Energy Security: International markets, industrial activism, technological failures, energy
price spikes, or competition for the scarce fossil fuels can disrupt the supply of fuel.
Producing liquid fuels from locally grown sources can mitigate the dependency on petroleum
products and provide energy security.
ii.
Sustainability: The raw materials of biofuels can be produced over and over again, unlike
fossil fuels, which are not renewable.
iii.
Economic Development: Production of biofuels would likely increase national exports, as
well as decrease imports of crude oil, stimulate the creation of jobs and income for farmers,
diversify the energy supply, and provide less expensive energy options from locally available
sources. The world energy demand is dramatically rising with global economic growth. “In
developing countries, energy consumption is predicted to increase by 84 percent by 2035, and
new sources of energy, such as biofuels, may have a role to play in meeting this demand3.”
iv.
Climate Change: Reducing greenhouse emissions is a way to address climate change as one
of the greatest global challenges. When the biofuels are combusted, they only release the
carbon dioxide that was absorbed during the plant’s growth, so they can be considered
‘carbon neutral.’ The overall process would require additional energy from an external source
1
"International Energy Outlook 2011"
Wassener, Bettina “Airlines Weigh the Advantages of Using More Biofuel”
3
"Chapter 1-Why Biofuels? Drivers for biofuels production"
2
1
that is not necessarily ‘carbon neutral’ for heating, separation, and compression elements, as
well as farming and transportation vehicles, but the emissions would be significantly less than
that of fossil fuel processes. Thus the biofuels can immediately lower GHG emissions.
1.1.2 Approach
In years past, corn has been mass-produced as not only a source of food but also as a source of ethanol, a
usable fuel base for automobiles. It is estimated that thirty-three percent of the corn grown in the United
States is used for livestock feed, with an additional thirteen percent being exported for the same purpose
in other countries. Fourteen percent of the corn grown in the United States is used directly for food and
beverage production. The largest portion of United States’ corn production, the remaining forty percent,
is used for the production of ethanol fuel4. This ethanol is mixed with gasoline that we use in vehicles, as
mandated by the federal government, in amounts up to 15%5. While this is a short term solution that is
prolonging the inevitable depletion of fossil fuels, team Dynamic Organics looked into the use of other
cellulosic material to produce a useable fuel. Some of the other cellulosic materials considered include
corn stalks, switch grass, Miscanthus Giganteus, and fast-growing poplar. Due to the annual yield,
robustness, and ease of processing/harvesting, Miscanthus Giganteus was chosen as the feedstock.
In the fall, we analyzed which of two processes would most efficiently produce a usable fuel. The first
process used an enzymatic approach to produce either ethanol or butanol from dried cellulosic material.
The second process utilized metal catalysts to produce the fuel via synthesis gas. If the synthesis gas
process were to be used, additional products, including alkanes, were able to be considered. The synthesis
gas route was chosen because of the variation in potential products, the availability of the information
regarding these processes, the equipment required of each process, and the costs associated with
developing start-up catalyst or enzymes.
This spring, Dynamic Organics has developed a preliminary design and economics for a facility which
converts dried Miscanthus Giganteus (MxG) into a mixture of gasoline blending components. The
process, split into five major units, includes a solid feed-handling system, a gasification unit, a
purification process to remove H2S from the synthesis gas, a product reactor system, and a final
separations unit. For each section, several alternatives were considered and based on this analysis the
optimal case was modeled in UNISIM Simulation Software and Polymath.
1.1.3 Benefit
Cellulosic material is already being used to produce biofuels on a small scale around the United States
and Canada. The development and optimization of a cellulosic to fuel process would make a very positive
impact on the environment. Depending on the scale to which cellulosic processes are utilized to meet the
growing petroleum need, the alternative fuels which are able to be produced with cellulosic material
would be able to alleviate some of the economic stress on the dwindling supply of petroleum being used
to meet a growing global energy need. Because the cellulosic material used in this process to produce
fuel are grown on unusable farmland or other land considered barren, they represent a promising source
of fuel and energy.
4
5
Carter
Healey, James R.. "Ethanol content of gasoline can be 15%, up from 10% now - USATODAY.com."
2
Furthermore, the renewable nature of using cellulosic materials allows annual replenishment of the
feedstock. While the use of cellulosic feedstocks may be somewhat dependent on the growing conditions
in a given year, the options considered in this study were well-known for being able to grow well in very
harsh conditions, including drought, heavy rainfall, or extreme temperatures. Because of this, these
feedstocks tend to be less dependent on variables out of the control of the manufacturer. These materials
require minimal maintenance, and are able to replenish themselves annually, if not more frequently.
Another benefit to the use of these cellulosic materials is the fact that these feedstocks can be grown most
anywhere. According to the US Department of Agriculture, grasses such as switchgrass or MxG are able
to grow throughout all of North America, from the northern regions of Quebec to the deserts of Arizona
and Nevada.6 This versatility allows these materials to be easily produced and processed anywhere.
1.1.4 Similar Case Studies
In the fall, Dynamic Organics researched just a few of the many alternative energy companies appearing
all over the country. The purpose of this analysis was to examine the way in which others have already
implemented cellulosic material into processes designed to produce energy.
In 2006, Alliant Energy’s Biofuels Development department did an area study of the feasibility of
building a power plant in Marshalltown, Iowa. The power plant was designed to use a fuel source of
cellulosic materials. A majority of the cellulosics were corn stalks and switchgrass. The corn stalks are
already a byproduct of corn and the stalks are often just left in the field. Based upon preliminary analysis
of the farm land encompassing the plant within a fifty mile radius, there is enough usable corn stalk
biomass to produce 50 times the necessary requirements to power the entire town of Marshalltown, which
is 5,900 MW7. The reason behind the choice of fifty miles as a constraint on the feedstock is that larger
distances would require a more significant transportation cost to move the pelletized feedstock from the
field to the facility. Another source of biomass that was considered was woody biomass products such as
trees, underbrush, and other cuttings. They also considered using switchgrass in their feed process.
Alliant designed their plant to be able to process all of the considered feed stocks at the same time without
having to switch any equipment. The official results of the feasibility study were not published
publically, but Alliant Energy did end up building a power plant that burns cellulosics in the area of
Marshalltown.
Dynamic Organics examined the feed stock of the process: Alliant uses mostly cornstalks, with some
added switchgrass and wood-based products as well. They are capable of producing up to 6.3 million
tons of biomass using only corn stalks and an additional million tons when also including switchgrass and
wood-based products. In order to power the town of Marshalltown, they only need 130,000 tons of
biomass8. According to the census of 2011, the city of Marshalltown has 27,775 residents and is growing
at a rate of about 1% per year8. The amount of biomass that it takes to provide such a large number of
residents with power is a little underwhelming. With such a large overproduction of feedstock in the
area, the town has opened up a compost pile to the public allowing them to drop off biowaste for a fee.
This fee ranges from $4 to up to $90 depending on how big the vehicle is and the type of materials being
6
7
8
United States Department of Agriculture
Johnson
"State and County QuickFacts"
3
discarded9. While this fee is not directly incorporated in our economics, the costs associated with paying
farmers for the feedstock is included, based on the current price of corn. Some of this material is used in a
compost pile to produce potting soil and the rest is used in their biomass reactor to produce energy.
Seeing what is capable from such a small reactor is very promising for use by Dynamic Organics.
Although the team did not intend to burn the feedstock, the amount of energy that is available by
combustion directly correlates to the amount of potential fuel that can be produced.
In 1955, Frysville Farms became involved with the U.S. Forest Service in order to have a continuous
mass product of hybrid poplars known as Schreiner Poplars. These hybrid trees have been developed to
provide a high density wood after a short amount of time. The poplar trees are big enough to harvest after
four years, and a new tree will then grow from the stump of the previous tree. The roots need to be
uprooted and replanted after thirty harvests, or one hundred twenty years10. Frysville Farms produces the
hybrid poplar in order to provide energy and heat from burning the wood. However, as poplar is a high
density cellulosic material, it is possible to use poplar trees as a potential feed into the process of
converting cellulosics to fuel. It is a very similar process in that they are producing a significant amount
of energy from a material that is not a food or otherwise widely grown material. Although the poplars do
require space for planting and growing and they can only be harvested every four years, they are capable
of producing a large amount of energy.
This feedstock is another encouraging possibility because of the potential amount of energy it can
produce per year. Also, these trees will grow right out of the stump of the previous tree, which means
that it is only necessary to plant the trees once and then it can continue to produce product for many years
after. It is a very dense wood which means that it is an excellent source of both energy and fuel, which is
also very promising. Just one acre can produce up to five cords of wood material every year. This
amount of wood, if burned, can produce 93.5 BTU’s or the equivalent of 730 gallons of fuel oil11. This is
not a direct correlation to the amount of fuel that Dynamic Organics would be able to produce from the
stock material, but it does serve as a rough estimate of the potential that can be obtained from such a
material. However, there are some potential drawbacks to hybrid poplars. Although they are very fastgrowing compared to other tree varieties, they cannot be harvested nearly as much as some grasses and
other potential feedstocks that can be harvested every year. Hybrid poplars are also less rugged than
many of the options considered. The trees require quality soil, with low pH and plenty of moisture. While
the plants can tolerate flooding for short periods, they also do not do well under drought conditions.
Hybrid poplars have some potential drawbacks, but there is a lot of promise in the amount of energy and
fuel that can be obtained from a wood material source.
1.1.5 Design Goals
In this project, Dynamic Organics demonstrated that the fuel products can be produced and that there is a
potential profit that can be made. To do this, the team designed a facility where a fuel source can be
produced on a large scale from a cellulosic feedstock using resources as efficiently as possible.
9
"City of Marshalltown"
10
11
"Frysville Farms"
"Frysville Farms"
4
1.2 Project Scope
This study can be summarized as an in depth feasibility study for a commercial scale process for the
conversion of biomass to fuel. This includes the selection of a feedstock, assembling and modeling of a
realistic continuous process, and an economic analysis designed to determine if this project is profitable.
1.3 Project Team Management
1.3.1 Introduction to Our Team
Team Members of Dynamic Organics are pictured below in Figure 1.
Figure 1. Team members from left to right: Aimee Diepstra, Philip Reinken, Mitchell Groenenboom, and
Kaylea Brase
Kaylea Brase grew up in Lino Lakes, Minnesota, where she loves to waterski and cycle. She spent the
summer in Lansing, Michigan interning with Granger Energy and Electric, which is a Christian familyowned business that utilizes landfill gas to run engines and generate electricity. At Calvin, she enjoys
running for the cross country and track team. She plans to become a missionary while using her degree to
aid in development.
Aimee Diepstra grew up in Grand Rapids, Michigan, where she graduated from Grand Rapids Christian
High School. While at Calvin, she spent a year as the president of Calvin College’s chapter of Model
United Nations and pursued her degree in both Chemical Engineering and Chemistry. During the summer
of 2012, she had an internship at Boehringer Ingelheim, one of the world’s leading pharmaceutical
companies, in Germany. After graduation, she hopes to find a job working in the petroleum industry.
Mitch grew up in Grand Rapids, MI where he graduated from Grand Rapids Christian High School.
While at Calvin, he pursued degrees in both Chemical Engineering and Chemistry. Since coming to
Calvin, he has done chemistry research for a Calvin professor as well as interning at a specialty chemical
5
manufacturing company: Bridge Organics. After graduating from Calvin, Mitchell plans to attend
graduate school at the University of Pittsburgh.
Philip grew up in Morton, Illinois where he graduated from Morton High School, while also developing a
passion for the game of tennis. During his pursuit of degrees in Chemical Engineering and Chemistry, he
has also competed on the tennis team at Calvin. He spent the summer interning with General Motors
Components Holdings in Grand Rapids, MI. After graduating from Calvin, Philip hopes to work in the
development of alternative energy or fuel sources, particularly in the petroleum industry.
1.3.2 Team Duties
The duties for each team member are as follows:
In the fall semester, Kaylea spearheaded much of the work to be done with a food waste to biogas project,
which was later determined to be out of the scope of the project presented in the following report. This
included meeting with several Calvin College Physical Plant employees and analyzing the feasibility of
the project in terms of the economics, including piping and compressor costs, and research into how the
digester and co-generation system at Calvin College operates. In the spring semester, Kaylea examined
possible processes for the synthesis gas purification unit. This involved extensive research into possible
processes, an examination of current EPA regulations, as well as modeling of the process in UNISIM.
During the fall semester, Aimee focused on the enzymatic process for the cellulosic to biofuel project. As
the enzymatic process was eliminated from the project scope, Aimee shifted her energy to continue work
with the cellulosic project to assist in the creation of a synthesis gas route. In the spring semester, Aimee
was responsible for the design of a product reactor system. This involved modeling of three possible
processes in UNISIM, a comparison of these systems, and extensive optimization work on the synthesis
gas to methanol reactor.
In the fall, Mitch analyzed the synthesis gas process in detail. His research focused on the creation of a
block flow diagram and determining as much as possible for the process. From initial conception to the
data presented in this report, Mitch was responsible for much of the work relating specifically to the
syngas process. In January, he designed and modeled the gasification process in UNISIM, and in the
spring, Mitch was responsible for the design of the methanol to gasoline reactor, the second reactor of the
production system. This will require major research as well as extensive modeling in both UNISIM and
Polymath.
Phil focused his energy in the fall semester on the many possible feedstock options. He researched other
facilities already undertaking the work of utilizing a cellulosic feedstock, and looked into the feasibility of
each feedstock to determine which might be most efficient and cost effective in the cellulosic to biofuel
project. Phil shifted his energy in the spring semester to the design of a feed handling system and the final
separation the product fuel from the other byproducts of the reactions. His work included both the design
and optimization of the equipment in UNISIM.
In order to ensure that the work was done thoroughly and correctly, a system of checks and balances was
established. Since each individual spearheaded a different avenue of the project, another person was
responsible for verifying the quality of this work.
6
1.3.3 Schedule Management
The details relating to the scheduling of the project, including a list of deadlines, a project task list, and
the project Gantt charts are available in Appendix B.
2. Decisions of Process and Feedstock
2.1 Overall Process
In the fall semester, two larger processes were considered to convert MxG to fuel products: an enzymatic
process and a catalytic process to convert MxG to synthesis gas.
2.1.1 Enzymatic Approach
Figure 2 is a block-flow diagram of the necessary stages of this process.
7
Figure 2. Block Flow Diagram of the Enzymatic Process
8
As displayed in the above figure, several steps are necessary to accomplish an enzymatic approach to the
creation of a usable fuel. Below is a step-by-step description of the process:
1. Storage and Handling Unit—Feedstock enters the process and is immediately sent here to be
processed in a shredder, which will result in a uniform feedstock for the rest of the process.
2. Pretreatment and Hydrolyzate Conditioning Unit—The uniform feedstock is moved into this area
of the process where lime, water, acid (likely H2SO4), and steam break down the cellulosic
material into gypsum (a byproduct), waste water and hydrolyzate.
3. Waste Water Treatment Unit—Waste water from the pretreatment unit is treated and the can be
recycled back to the pretreatment unit here. This can be accomplished by utilization of enzymes
or bacteria, similar to methods done in governmental waste water facilities.
4. Saccharification and Co-Fermentation Chamber--The hydrolyzate is transferred here where an
enzyme and some enzyme nutrition is added to break down the complex sugars which make up
the hydrolyzate. The resulting products are gases, which is vented from the unit, and a broth
mixture containing fuel product as well as additional water and other impurities.
5. Product Separation Unit—all components leaving the saccharification and co-fermentation
chamber are transferred here and additional steam is added to aid in the separation. This unit
likely will contain a centrifuge to remove solid lignin to be used as a fuel source, followed by
separation units that will separate the waste gas to be vented from additional water and product
fuel.
The difficulty with this process is largely related to avoiding azeotropic mixtures of product.
Specifically in the case of an ethanol and water mixture, there are major limits associated with the
purity of the product that would be possible to obtain. The key of this step is to avoid these
azeotropic conditions and to limit the amount of equipment necessary to obtain an appropriate
level of ethanol/water separation.
6. Burner/Boiler Turbogenerator—lignin, the solid waste leaving the separation unit, is sent here to
be burned in order to create electricity.
7. Purified Product Storage Unit—purified product fuel is sent here to be housed until ready to be
utilized.
2.1.2 Synthesis Gas Approach
A second alternative process exists which functions by taking in dried plant material and creates fuel via
the production of synthesis gas. Figure 3 displays a block flow diagram of this process.
9
Figure 3. Syngas Process
10
As shown in the figures, multiple steps are required for either process, below is a step by step description
of the synthesis gas process.
1) Feed Processor – This process involves the drying of wet plant matter as well as the compressing
of the dried material into pellets, which will be used as feedstock for the facility. This process
was assumed to take place as the harvesting of the plant material was occurring, therefore was
considered out of the scope of our project.
2) Feed System – As the remainder of the system is to be pressurized a specialized feed system must
be utilized for the cellulosic pellets to be added to the process, without causing a back flow of
synthesis gas through the system. A cyclone with an outlet to a screw conveyer system was
determined to be the optimal choice for this system.
3) Pre-heater – raising the temperature of the plant material mixture to be suitable to enter the first
reactor. This is likely done through pressurized steam or another heat transfer medium.
4) Reactor 1 – Dual Fluidized Bed Reactor – The first reactor gasifies the majority of the plant
material into a combination of CO2, CO, H2, CH4, H2O and tar. This reaction is catalyzed by a
Nickel or Iron based catalyst, and requires a temperature of 980oC and 1.7 bar.
5) Cooler – The reactor 1 effluent is cooled to approximately 200 -350oC using either cooling or
saturated water or another heat transfer medium, to reach the operating conditions of the synthesis
gas purification system
6) Synthesis Gas Purification system – Utilizes methanol to remove H2S from the effluent of reactor
2, to ensure the maximum life of catalysts downstream in the process.
7) Reactor 4 – Product Formation – Reactor 4 serves converts our freshly purified amounts of CO
and H2 into the final product through the use of a catalytically driven process. These require some
type of metal catalyst, and temperatures in the range of 150-300oC and pressures of 20-95 bar.
8) Separation Equipment – Serves to purify the product to fuel standards.
2.1.3 Process Decision
Both the synthesis gas and enzymatic processes have been done successfully in industry12. In the fall, we
proved that based on our research, it is clear that both of these processes would be feasible given the
proper time and resources. Because the nature of this project is that it can require no more than
approximately nine months and must fall under a strict budget, the feasibilities of each project were
reconsidered with this is mind.
In an attempt to quantify which process would be more feasible to accomplish within the constraints of
time and budget on this project, a decision matrix, shown below, was created. The result of this endeavor
was that the synthesis gas process would be significantly more feasible.
12
Philippidis, George P., Tammy K. Smith, and Charles E. Wyman. (1993).
11
Table 1. Decision matrix for determining a process route.
Synthesis
Route
Weight
Enzymatic
Synthesis Gas
Budget
Effectiveness
8
5
7
Necessary
Equipment
6
6
8
Technological
Availability
10
5
9
TOTAL
SCORE
126
194
Each category of interest was weighted and ranked on a 1-10 scale, with a 10 being a high score and a 1
being the lowest score. As a result of the decision matrix, the synthesis gas method was a clear winner
with a score of 194.
2.2 Process Feedstock
Feedstocks were researched extensively in search of a cellulosic material that was both fast-growing and
could be grown in poor soil unable to be used for food farming. Dynamic Organics narrowed the options
to four possibilities: switchgrass, Miscanthus Giganteus, fast-growing poplar, and corn stalks.
2.2.1 Switchgrass
Switchgrass and your suburban lawn grasses—bluegrass and zoysia grass— are very different.
Switchgrass is big and it's tough—after a good growing season, it can stand 10 feet high, with stems as
thick and strong as hardwood pencils13.
What makes switchgrass bad for barefoot lawns makes it ideal for energy crops: It grows fast, capturing
lots of solar energy and turning it into lots of chemical energy— cellulose—that can be liquefied,
gasified, or burned directly. It also reaches deep into the soil for water, and uses the water it finds very
efficiently. And because it spent millions of years evolving to thrive in climates and growing conditions
spanning much of the nation, switchgrass is remarkably adaptable.
Switchgrass is a perennial grass native to the tall grass prairies once found across much of the U.S.A.
Today it is grown mainly as a forage crop or as ground cover to control erosion14. Switchgrass is grown
and distributed throughout North America and is frequently used in the Conservation Reserve Program
and wildlife habitat programs15. Its rapid growth rate and winter hardiness make it an attractive biomass
crop for biofuel production. The crop can be burned as an energy source for producing grain ethanol or
fermented into biofuel.
Switchgrass can grow in many climates of varying temperature and dryness. Figure 4 displays locations
within the continental United States where switchgrass grows readily. The vast variation in location for
switchgrass is an indication of its ruggedness and adaptiveness to any surroundings.
13
Elbersen, Dr. Ir. H. W.
Jimmy Carter Plant Materials Center. 2011.
15
"Switchgrass - Alamo and Blackwell Switchgrass Seeds for Sale - Warner Brothers Seed Company."
14
12
.
Figure 4. Switchgrass growth in the United States.16
The nominal switchgrass yield is approximately seven tons/ha17. Table 2 compares this yield to other
common biomass crops found throughout North America.
Table 2. Nominal annual yields of biomass crops18
Biomass
Crop
Geographical
Location
Annual Yield
(kg/ha)
Corn Stover
North America
7,000
Switchgrass
North America
14,000
Hybrid Poplar
North America
14,000
2.2.2 Miscanthus Giganteus
Miscanthus Giganteus (MxG) is one of the most promising biomass crops for energy utilization. For
about ten years, several works have been published regarding MxG as materials for combustion or
pyrolysis19. Its renewable properties contribute to its strengths. Because carbon is completely fixed during
growth of C4 plants like Miscanthus, MxG has an almost zero net CO2 emission.
Miscanthus is a genus of hardy perennial grass species native to parts of Asia, Polynesia, and Africa
producing very high yields of bamboo-like cane up to 4m tall. Yields found in the literature range from 15
16
Land, Graham. "Switchgrass – America’s own biofuel. But is it any good? Greenfudge.org." Environmental News
With a Sticky Twist - Greenfudge.org.
17
Ha, Su, and Bernie Van Wie. "The Biorenewable Resource Base."Introduction to Biofuels.
18
Ha, Su, and Bernie Van Wie. "The Biorenewable Resource Base."Introduction to Biofuels.
19
Clifton-Brown, J.C., J. Breuer, and M.B. Jones. 2007. “Carbon Mitigation by the Energy Crop, Miscanthus.”
13
to 40 tons/hectare20. Miscanthus can be harvested every year for up to twenty years on the same rhizome,
or seed. If harvesting is done early, the environmentally friendly crop, which requires little or no pesticide
or fertilizer, contains less than 15% moisture.
Miscanthus has received attention as a biofuel crop because it has relatively high dry matter yields across
a range of environmental and soil conditions. The Miscanthus genotype most commonly recommended
for biofuel production is a sterile hybrid (Miscanthus x giganteus) believed to be a M. sacchariflorus x M.
sinensis hybrid. Miscanthus x giganteus probably originated in Japan and was brought to Europe in 1935.
It subsequently spread throughout Europe and more recently North America.
The bioenergy industry has primarily used Miscanthus for combustion in power plants. It has desirable
properties of low water and ash contents following a dry-down period before harvest. Current research is
focused on its potential as a biomass crop for direct combustion and for lignocellulosic conversion to
ethanol and other biofuels.
Miscanthus x giganteus is generally established vegetatively from rhizome pieces, resulting in high
establishment costs. Recent research has indicated that it may be propagated vegetatively from stem
cuttings. Presently, no commercially available mechanical planters can be found in the United States, but
potato or similar planters have been modified for planting Miscanthus x giganteus rhizomes. Rhizomes
should be planted approximately 2–4 inches deep, 3 feet apart within rows, and 3 feet between rows
(approximately 4,840 rhizomes per acre)21.
Total water use can be relatively high because of high overall biomass production, but Miscanthus is
relatively efficient with water use for a warm-season perennial grass. As with other perennial grasses,
Miscanthus has been shown to increase soil carbon. Miscanthus x giganteus is not native to the United
States, but it was assessed as not a problem species using the IFAS Assessment of Non-Native Plants in
Florida's Natural Areas (IFAS Invasive Plant Working Group 2009)22.
Miscanthus can grow well in a variety of climates. Figure 5 displays an approximate growing range for
Miscanthus.
20
Gibson, L., and S. Barnhart. 2007. Miscanthus Hybrids for Biomass Production.
UF/IFAS Invasive Plant Working Group. 2009. IFAS Assessment of Non-Native Plants in Florida’s Natural
Areas.
22
"UF - IFAS Center for Aquatic and Invasive Plants."
21
14
Figure 5. Approximate growing location for Miscanthus Giganteus.23
In terms of composition, MxG is well organized and contains 43%wt cellulose. It contains an average of
2% of mineral part, and no more than 0.2%wt. elemental sulfur. A full breakdown of MxG composition
can be found in Table 3.
Table 3. Composition of Miscanthus Giganteus, presented by Michel,Gruber, et. al.24
Using the data presented in Table 3, the pyrolysis and gasification of MxG was modeled in UNISIM, and
is presented in a later section of this report.
23
Heaton, Emily A.. "Miscanthus (Miscanthus x giganteus) for Biofuel Production - eXtension." eXtension Objective. Research-based. Credible..
24
Gasification of Miscanthus X Giganteus in Catalytic Conditions Production of Syngas, Preliminary Results
15
2.2.3 Fast Growing Poplar
Hybrid poplars are commonly classified as short-rotation woody crops and can be grown on forest lands
or on economically marginal crop lands. Clonally propagated trees are harvested with conventional
forestry equipment and delivered to processing facilities in the form of chips. Distribution maps of the
genus Populus and the species P. deltoids and P. tremuloids presented in Figure 6 show a very wide
spatial distribution of hybrid poplar in the USA and Canada. Given the widespread distribution of poplar
in the USA, suitable species or hybrids can be chosen for cultivation close to processing facilities in any
region.
Figure 6. Distribution of poplar species in the USA and Canada.
(A) Populus (genus) (B) P. deltoids, (C) P. Tremuloids.
The nominal yield (including moisture content at harvest) of hybrid poplar species in North America is
estimated to be 14 Mg ha–1 year–1 This is comparable to that of switchgrass (14 Mg ha–1 year–1) and
much higher than corn stover (8.4 Mg/haᵒyr) and wheatstraw (6 Mg/haᵒyr). In Quebec, yields of 17.3
Mg/haᵒyr were obtained without fertilizers or irrigation25. Upon maturity, poplar species can grow up to
approximately 26 m in height and 60 cm in diameter. These yields are compared to other common
biomass crops in Table 2.
25
"Fast-Growing Poplars Provide Solutions for Both Energy and Pollution Problems."
16
2.2.4 Cornstalks
In production of ethanol, the corn kernel is commonly used; however, this is a potential misuse of a
product which is food. In the USA, corn, or maize, is a key candidate for ethanol production. Figure 7
displays the approximate locations in which corn is grown in the USA.
Figure 7. Corn Production Regions in the United States26.
It yields corn grain which is converted to ethanol. The potential for ethanol from maize lies not only in
converting the grain to ethanol, but also in applying cellulose conversion technology to the pericarp that
covers the grain. Cellulose conversion technology, consisting of pretreatment and hydrolysis, offers the
prospect of extending conversion to other parts of the corn plant, such as corn stover (cobs, stalks, and
leaves). Significant increases in the ethanol yield per acre of maize harvested are possible if biomass from
the maize residue is utilized for ethanol production. A quantitative analysis of mass balance has been
carried out to address this issue.
The corn cob, stalks, and leaves can be converted to fermentable sugars with cellulose processing
technology27 that consists of pretreatment, hydrolysis, and fermentation using yeast or other
microorganisms which were being considered for the enzymatic process. In contrast to grain-based
feedstocks, cellulose-based ethanol production requires microorganisms that are capable of producing
ethanol from both glucose and xylose.
Corn grain contains high amounts of starch, which is readily convertible to monosaccharaides upon
pretreatment (i.e., cooking in water) and hydrolysis. Glucan is also present in the cob, the stalk, and the
leaves, but in a different form, i.e., cellulose, and at lower amounts compared to corn grain.
26
"USDA ERS - Commodity Costs and Returns: Documentation." USDA ERS - Home. United States Department of
Agriculture,
27
Schirber, Michael. " Corn Stalks Engineered to Self-Degrade into Fuel | LiveScience ."
17
2.2.5 Feedstock Decision
Plants are able to have a variety of different cell structures and thus different types of plants can have very
different properties. Some of the more crucial aspects for our design project include the cellulose
composition, hemicellulose composition, usable ethanol content, and the production or growth density
rate. These values were gathered from Bioenergy’s article as well as Sannigrahi’s article28 and compiled
in the following table.
Table 4. Selected Composition and Production Data for Possible Feedstocks
Plant Source
Production (Tons/acre) Cellulose Content (wt%) Hemicellulose Content (wt%) Ethanol (gal/acre)
Switch Grass
4.6
40
29
421
Miscanthus Giganteus
13.2
45
24
1198
Corn Stalks
3.3
38
26
300
Poplar
8
45.1
21.5
1050
While each feedstock has the potential to be used in either the synthesis gas or the enzymatic process, the
degree of success associated with a specific feedstock and a given process can vary depending on plant
composition. The cellulosic content, as well as the ash content, can have varied effects on the overall
efficiency of a process. Because these compositions are used in determining the reaction kinetic model,
the compositions of the plant material can more or less determine the success or failure of a process.
In order to select the most appropriate feedstock of cellulosic material for our process, we developed the
decision matrix based on relevant criteria determined both through research and our relevant design
norms. We then assigned a weight to each category based on the importance to our project and ranked
each cellulosic source in each category, the plant with the highest weighted score is then the most ideal
plant to be used. The four plants to be critiqued were switch grass, corn (stalks), Miscanthus Giganteus
and fast growing poplar. Each of these plants was shown to be capable of relatively high yields of
biomass in a given year, with the exception of cornstalks which is present as a control, as it has been the
standard source of biomass in the past.
Table 5. Decision Matrix for Feedstock Selection.
Source of
Fuel
Switch
Grass
(Tons/acre)/year
produceable
Ruggedness
Ease of
Waste
Ease of
Total
Proccessing management Harvesting Score
4
10
10
2
3
7
Corn Stalks
Miscanthus
Giganteus
Fast growing
poplar* **
Weight
28
6
6
7
161
10
88
7
177
5
133
5
10
5
8
6
6
5
10
8
2
Sannigrahi, P.; Ragauskas, A. J.; Tuskan, G. A.
18
4
5
3
Comparing yield, ruggedness, ease of processing and harvesting, Miscanthus Giganteus was calculated to
be the ideal feedstock.
2.3 Other Design Notes
The facility designed by Dynamic Organics can be separated into five sub-units. For each of the subprocesses being designed in the cellulosic material to fuel project, there are many different options that
were optimized and considered in depth.
In order to analyze and compare each alternative, the team modeled each process and took into
consideration the economics, safety, and design norms directing the project. All of these factors weighed
into the decision making process displayed in the design of each piece of equipment. Throughout the
process of decision making, the team consulted these factors and thought about the tradeoffs that must
occur within the design process.
One of the major factors that Dynamic Organics considered is the economic analysis of the chosen
process. The process must not only be financially beneficial, but it should be able to be a potential
replacement for fossil fuels in the future. Regardless of product selection, the product must be worth
more than the amount of money it takes to produce it. The team designed the processes in such a way that
the final product was able to be sold at a price that is competitive with the current prices of gasoline.
The quality of the gasoline produced by our design has an octane number of 105. This is significantly
higher than that of unleaded or even premium gasoline currently on the market. With an octane number
this high, the amount of gasoline components produced by our system that would be required to bring
unleaded gasoline up to premium grade was calculated and used, along with current gas prices29, to
determine the appropriate price for our product. Assuming approximately a $1.00 up-charge for gas
station profits, the total cost per gallon of gasoline produced by our system is approximately $3.65. This
is competitive with today’s current gas prices. A full explanation of the calculations done can be found in
Appendix G.
Dynamic Organics considered safety to be one of the most important considerations in determining
whether or not a particular design feature is feasible or not. Because the team was planning to produce
fuels in bulk quantities, safety became a large factor. Some of the intermediate products and byproducts
have high flammability and/or toxicity and must be contained and disposed of in a responsible manner. In
addition, high temperatures (upwards of 500oC) and pressures (20-25 bar) were used throughout the
processes and therefore, safety must be a priority when designing these features, both in materials of
construction and control methods.
In every aspect of the design process, design norms played a major role in determining whether or not a
design was feasible and sufficient for implementation. Transparency is a consideration throughout each
process. Dynamic Organics believes it is vital to identify and design around any potential safety factors
in each of the design processes. Dynamic Organics believes the design norm that they best represent is
stewardship. By utilizing an inedible, unusable product grown in poor soil, in order to create a fuel that
will be able to power vehicles, power plants, and heating systems, we believe that by exploring this
29
"GasBuddy.com - Find Low Gas Prices in the USA and Canada."
19
project, we are caring for God’s creation by conserving valuable fossil fuels and utilizing a renewable
source instead. Additionally, by utilizing a renewable resource, we are effectively reducing the carbon
footprint of the petroleum industry. Furthermore, by designing a process which reliably produces
gasoline from a renewable feedstock, we are protecting future generations from experiencing a global
energy crisis and are instilling in our design a level of trust, as it is clear that we are working to benefit
society with our design. The idea of justice is apparent in our design as well. Because we were not willing
to consider the use of a food source as our feedstock, we are making a statement that suggests the right of
everyone to proper food supplies, and promoting the proper use of our food resources. Production of
ethanol via corn, on the other hand, shortens grain supplies and contributes to malnutrition. “The grain
required to fill an SUV’s 25-gallon tank with ethanol just once will feed one person for a whole year30.”
In addition, though efficiency is improving, at first the process consumed more energy than was available
in the resulting fuel. Corn ethanol is transported over 1,000 miles in a tanker only to be pumped into an
E85 flexfuel vehicle which fails to deliver 20 miles per gallon. The success of our design can be marked
by how well it eliminates these problems. Finally, by producing a product which can be introduced
directly into current gasoline engines, we are exhibiting the design norm of cultural appropriateness,
recognizing that we cannot easily change the vehicles relied upon across the nation.
3. Feed Handling System
3.1 Site Determination and Equipment
One of the key design variables that we are working around is that we do not want the production of our
cellulosic material to take away from a potential food source. Therefore, we are ensuring that the plants
to be used in the process are not edible, as described previously, and that they are able to be grown in a
desolate and barren environment that cannot support a food crop. Because we are intending this project to
take place in the United States, it was important to seek out suitable locations for such a chemical facility.
In order to determine whether or not an area would be suitable or not, it needed to possess the following
attributes: barren and desolate land, access to some water source, and enough land within a fifty mile
radius of the necessary type to facilitate the feedstock necessary for our biofuel facility. It was necessary
to look for locations within a fifty mile radius in order to account for transportation convenience and cost.
Using a government program known as CropScape31, which was designed by the National Agricultural
Statistics Service branch of the United States Department of Agriculture, we sought out suitable locations
around the United States that fit the criterion for a location in order to ensure that the project is feasible.
With this online tool, we were able to select circles of land that were approximately fifty miles in
diameter and receive statistical data of what was grown in that area within the last year. The statistical
charts contained the acreage of each agricultural or developed area within the area of the circle. Two
different types of areas were sought out: one for a facility that would use corn stalks as the feedstock and
one that would use Miscanthus Gigantus as the feedstock. While Miscanthus is the preferred and desired
feedstock, corn was sought out as a back-up analysis solution.
30
31
Lester R. Brown
"CropScape - Cropland Data Layer."
20
We found that both projects would be both possible and practical, but because Miscanthus is our primary
focus, we will look at only the potential locations for it. Potential Miscanthus biofuel locations could be
implemented in Texas, Arizona, Oklahoma, Nebraska, Kansas, Montana, California, New Mexico, South
Dakota, Nevada, Oregon, or Wyoming with virtually unlimited numbers of possibilities in each state by
simply shifting the circle a few miles in any direction. Each of these states has a plethora of areas where
there is enough desolate and barren shrub land within a fifty mile radius to supply the feedstock necessary
for the biofuel facility. These areas also have a water supply within a few miles of the potential crop land,
which make them suitable for growth of the Miscanthus. One example of a potential location radius and
its corresponding table of agricultural land use is shown in Figure 8 and Table 6 respectively.
21
Figure 8. An example of the size of a prospective area for a refinery from CropScape32
32
"CropScape - Cropland Data Layer."
22
Table 6. Table of acreage and land usage within the sample area in Figure 8
Category
Corn
Sorghum
Soybeans
Sunflower
Pop or Orn Corn
Winter Wheat
Rye
Oats
Millet
Safflower
Alfalfa
Other Hay/Non Alfalfa
Sugarbeets
Dry Beans
Potatoes
Peas
Fallow/Idle Cropland
Open Water
Developed/Open Space
Developed/Low Intensity
Developed/Medium Intensity
Developed/High Intensity
Barren
Deciduous Forest
Evergreen Forest
Mixed Forest
Shrubland
Grassland Herbaceous
Woody Wetlands
Herbaceous Wetlands
Dbl Crop WinWht/Corn
Acreage
15,194.00
233.70
732.80
55.80
37.80
2,564.00
748.40
108.50
1,388.90
0.20
8,710.10
3,767.80
1.30
191.70
740.80
0.70
451.90
49,257.50
6,151.90
3,726.20
271.30
40.30
809.10
1,424.40
6,732.30
4,384.30
72.50
4,743,660.50
14,413.00
176,326.90
13.30
Cropscape not only displays an accurate depiction of the land use with colored pixels on the map, but it
also gives a numerical report as shown above of the land usage by the number of acres. The CropScape
reports of agricultural land use show that these areas contain mostly shrubland and herbaceous grassland.
With 4.75million acres of herbaceous grassland within the 5.1million acre circle, there are numerous
potential locations available to us for potential farmland. Some of this grassland is currently being used
for cattle ranging, however, there is more than enough land to use some of this area for Miscanthus
production. Gaining the trust of local farmers and ranchers would be important in order for the facility to
23
be a success. They would have to be willing to either sell some of their land or produce the crop that we
desire, Miscanthus Gigantus, in order to even gain a feedstock. Additionally, the people who are not
involved in the production process will need to trust the facility to be safe and that if something were to
happen, they would be protected physically and financially. This could potentially be a concern when
looking into our engineering design norms, which include trust and caring. We hope that people will be
able to trust us, but if they do not we must care for their needs by building elsewhere or providing them
with whatever they may need to set their minds at ease.
With numerous potential locations spread throughout the United States, a biofuel facility is feasible and
therefore the next step is to ensure that the farming of the cropland is also feasible. For this type of
farming process, the technology already exists for growing and harvesting wheat, grains, and several
other types of grasses. One particular element that will also need to be implemented into the farming
process is a pelletizer. A pelletizer would crush the grass as it is harvested and deposit the pellets, about
the size of a kidney bean, into a wagon or trailer via an auger. A pelletizer would be a relatively large
expense for a farmer, but much like a combine or a plow, it would be an investment that would last a long
time. The pelletizer unit shown in Figure is a unit built by a start-up business called Lawson Mills
Biomass Solutions33 and is capable of producing up to 1000 pounds per hour. Their recent appearance in
the Farm Show, a magazine for innovative farming methods and technology, has given their business the
recognition they need to stay in business. Lawson Mills claims that they can make pelletizing units to any
specifications, including pelletizers that would pull behind a harvesting tractor much like a baler. Much
like the corn and grain industries, the farmer could purchase a pelletizer, which would be reflected in the
price that they charge for the pelletized Miscanthus product.
Figure 7. The founder of Lawson Mills Biomass Solutions and one of his pelletizer units34
We suggest that they purchase one from Lawson Mills due to the convenience and flexibility of volume
output that they provide. We determined that it would be best if the farmers were to pelletize the material
before shipment because it would be easier to transport, it would be more compact, which implies greater
mass per truckload, and it would take a majority of the water out of the plant material by crushing it out.
33
34
"Start A Business With Pelletizer ."
"Start A Business With Pelletizer ."
24
Additionally, by crushing the plant into a pellet at the refining facility, we would be transporting less
usable material per truckload, which is a large financial waste, and it would make the transportation easier
due to a higher density of product.
The input to the cellulosic-to-biofuel process is required to have as little water as possible, because water
disrupts the gasification process and causes excess char to be produced. Because some char is necessary
for the heating of the sand in the gasification reactor, the amount of moisture in the feedstock could
provide a method of controlling the optimization of the first reactor in the process. With additional
drying, the reaction could be pushed further toward the products or more moisture could be left in for
fewer products depending on whether more char is needed for heating. However, if pure, raw Miscanthus
is fed to the reactor without any sort of modification, the system will fail due to too much moisture and
inconsistent solid size. Therefore, the pelletization process will be another factor that must be accounted
for as the feed is shipped in from the farms. Sometimes the feed will be more moist and other times it
will be less moist in addition to sometimes being more densely pack and other times being less densely
packed. Therefore, quality control checks will be put into place that will predict minor deviations caused
by inconsistent feed moisture, density, and composition and control such deviations accordingly.
3.2 On-Site Facilities and Equipment
The pelletized feedstock will be transported to the facility in trucks directly from the fields and stored in
large storage bunkers much like corn and beans. It will be transported to the storage bunkers using an
underground to above the bunker elevator conveyor system as shown with a truck unloading in Figure 9.
25
Figure 9. A storage bunker with a truck unloading into an elevator conveyor system35
The storage bunkers will provide temporary storage of up to two months’ worth of the pellets as well as
facilitate the first step in the reactor feed system. Because a Miscanthus field can be harvested at full
capacity once a year, the fields harvested will be rotated so that the facility will have a continuous feed to
the plant with some storage available for any down time. Because of the locations being considered, it is
a reasonable assumption to say that a Miscanthus field can be harvested at various times of the year and
provide the same amount of feed stock. The storage bunkers will deposit feedstock down onto an elevator
conveyor belt, which is a conveyor belt with high ridges on it that can move large amounts of material
vertically on each tray as shown in Figure 10.
35
"Large Volume Grain Storage Systems Using Precast Concrete Bunkers."
26
Figure 10. A small elevator belt system that would be used to transport solids36
The ridges on the conveyor belt allow for material to be pushed up the belt and can be scaled to any size
that is necessary. The elevator conveyor belt needed for this process would need to be much larger.
Currently, such conveyor belts are being used for the coal power generation industry and are readily
available to be implemented into our process.
The next stage of the solid feed system is a regulatory tank. Similar to a silo, this is a large cylindrical
container that would contain about an hour’s worth of the feedstock pellets, which is roughly 1250m3, and
an outlet at the bottom that would let out pellets at a controlled rate. This would be controlled by a valve
that has a cascade feedback control from the reactor and the height of the pellets within the feed silo. The
capacity will generally remain halfway full for volume control purposes as a half full feed control bin is
the industry standard in chemical engineering37. Thus the feed silo will be 2500m3, which is rather
common and almost on the small side when compared with the solid feed system of the coal industry.38
The coal industry uses these silos extensively to control the feed rate of solid coal to the furnaces in
power plants, shown in Figure 11.
36
"KONVEYÖR BANT."
Bequette, Wayne B.
38
"Company Profile." Roberts and Schaefer.
37
27
Figure 11. A feed silo used in the coal industry to control the feed to the furnaces39
In the picture above, there is a conveyor stream going into the feed silo from the back and pouring into
the top. At the base of the feed silo, you can see where the solid will then be sent on to the next part of
the process. It is likely that, much like the coal industry’s feed silos, there would be some sort of stirring
or agitation within the feed silo to prevent clogging or clumping, which will likely occur even more so
than the coal industry due to the size of the pellets comparatively with bricks of coal. Despite being
important design factors for our project and concerns if this facility will be built, this aspect is not an
important part of our design and requires no further detail. The important details are that it exists, it
would work, and it will be a minor cost that will be factored into the economics section.
The final stage of the solid feed system, where the pellets will be fed into the reactor, had three different
options that have been used in industry. The feed can be fed by gravity, by pressurized gas and
pneumatics, or by an auger screw. Each of these options has its pros and cons that we carefully
considered in order to come to a final decision on which to use.
The gravity fed method would utilize the feed silos elevated location to the advantage of the process.
This method would have a slanted pipe that would exit the feed silo and lead directly to the first reactor
via a valve or gate system. This feed mechanism is the least expensive of the three methods because it
would simply take a controller and a valve. These types of systems are common in agricultural seed
packaging and automatic animal feeders as shown in Figure 12.
39
"Company Profile." Roberts and Schaefer.
28
Figure 12. A gravity feed hopper used for a cattle feed system40
The positive features of this design are that it would be very cost effective in terms of both capital costs
and in terms of energy usage over time and it should effectively work in that the solids would reach the
reactor. The negative features of this design are that it would be a rather inconsistent flow because it
relies wholly on the uncontrollable, though very consistent, variable of gravity, there is a severe danger of
product gas escaping back up through the feed stream, and there is the worry that an overabundance of
oxygen could go into the system, which could make the reactor unstable and possibly make it explode.
Another type of feed system is an inert air powered pneumatic feed, which would use pressurized air to
force the solid feed into the reactor. The feed silo, in this case, would most operate in a similar way as the
gravity fed method with an additional feed hopper in order to have a consistent flow of solid pellets,
which would be forced into the reactor with a controlled air stream as shown in Figure 13.
40
"Model 40 Gravity Hopper."
29
Figure 13. Solid Feed Eductor System for a Pneumatic Feed Design41
The positive features of this design are that it would provide the most consistent feed flow rate to the
reactor, which is conducive to a consistent product, it has low operating costs as the only major operating
cost is the compressed air system, and it would prevent the backflow of product gases into the feed
entrance because of the pressure. There are also negative features of this design. The initial capital costs
would be rather high comparatively to the other feed systems, but it would be an almost irrelevant
difference comparatively to the cost of the whole plant. It would require an additional feed hopper and
the parts would wear out faster due to friction for this section, which may lead to additional down time of
the facility. The reactor would require either an inert or excessive amounts of oxygen to be entered into
the system to push the pellets into the reactor. Separating the inert out would require more energy as well
as the excess energy to heat the additional material. Using air or oxygen as the gas would cause a risk for
explosions within the reactor as excess oxygen will cause complete combustion of the feedstock.
Additionally, this technology is untested on the industrial scale that we are considering and thus we are
reluctant to try something that has only worked on desktop models without a pilot plant.
The third solid feed system that we considered and eventually chose to implement into our designs is an
auger feed system. The industry standard in coal power, the auger feed system uses a screw mechanism
to push the solid feed into the reactor. Generally, due to the sensitivity of coal power plants to the fuel to
gas ratio of the feed, the auger contains vacuum lines along the way to take out as much air as possible.
The amount of gas that is let into the system could be optimized to make the first reactor more or less
efficient depending on whether it needs to produce more char or more synthesis gas at the time. The
41
"Solids Handling Eductors Pneumatic Conveying Eductors for Handling Dry Solids."
30
auger screw system uses mechanical energy to force the pellets into the reactor at a controlled rate as
shown in Figure 14.
Figure 14. A screw conveyor feed system made by Wolf Industries42
The aspects of this design that are beneficial are that it will provide a consistent flow rate of the feed
pellets, the product gases will not come back through the feed because it can run pressurized, and excess
air will not be forced into the reactor. Because we are only feeding into a 1.7atm reactor, the pressure
differential is not high enough to implement a large compression system. The consistent mechanical feed
with the densely packed pelletized feedstock will be a solid enough of a barrier to hold the vessel at its
specified pressure. The primary drawback of this system is that it would have high capital costs and it
would have high utilities costs due to the power necessary to turn the feed screws. The reason that we
chose this system is because it met all of the needs of our project’s feed stream by providing a consistent
feed rate, it does not allow products to go back up the feed stream, and it limits the amount of air into the
reactor feed.
The proposed flow diagram for the feed handling system is displayed below.
42
"Screw Conveyors Engineered Equipment."
31
3
Elevated Conveyor
W-100
Feed Auger
C-100
2
4
1
5
Auger Screw Conveyor
W-101
Cellulosic Storage Tank
S-100
Feed Handling
Figure 15. Schematic of Feed Handling Process.
32
4. Gasification
Gasification is a process where carbon containing materials such as wood, coal or biomass are heated to
high temperatures and allowed to decompose into a mixture called synthesis gas or syngas. This syngas is
a reactive mixture of mainly carbon monoxide and hydrogen, which we can later convert to other fuel
products. For the purpose of this report biomass, specifically Miscanthus Giganteus, is the target reactant
due to reasons discussed in earlier sections of this report. The type of physical equipment to be used and
process must be modeled uniquely depending on the feedstock to be used. In the case of biomass, this
consists of four steps43:
Feed processing and handling: discussed in the feed handling section of this report.
Pyrolysis: A pyrolysis is a decomposition of biomass, where the biomass fed to the system decomposes
into a variety of hydrocarbons, olefins and aromatic compounds. We modeled this via correlations since
no accurate understanding of the kinetics has been found in literature.
Secondary Kinetics determine the reactor effluent composition: The pyrolysis product mixture is
converted to a synthesis gas mixture through a series of kinetically controlled reactions. These vary by
reactor technology and feedstock. These kinetics consume the larger hydrocarbons generated through the
pyrolysis to produce a carbon monoxide and hydrogen-rich synthesis gas. This process also produces a
non-ignorable amount of char.
Char oxidation: Char, carbon compounds with a similar chemical make-up to coal, is removed from the
main reaction vessel and is combusted for energy and to ensure that the char does not contaminate later
processes.
4.2 Options for Gasification Processes
There are several commercially available technologies for the gasification of biomass: fixed bed gasifiers,
fluidized bed reactors, and dual-fluidized bed reactors44.
Fixed bed gasifiers have a fixed bed of carbonaceous fuel through which a gasification agent (steam or
air) is passed. This can be performed in either a counter-current “updraft” or concurrent “down-draft”
manner, the design of these are very similar differing only in the location of oxygen input. In the
countercurrent example the fuel is fed from the top of the reactor, while air (the gasification agent) is fed
from the bottom45.
43
Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P., Verdier, G.; Dufour, A. Energy and Fuels, 2012, 26, 3840-3855.
44
Malkow, T.; Waste Management, 2004, 24, 53-79
"Description of the biomass CHP technology based on biomass gasification." Bioenergiesystem, n.d. Web. 18
Apr. 2013. <http://www.bios-bioenergy.at/en/electricity-from-biomass/biomass-gasification.html>.
45
33
Figure 16. A countercurrent fixed bed Gasification unit
The biomass then falls through the hot air, and is converted to synthesis gas through this contacting. The
formed synthesis gas then proceeds out through the top of the reactor in a gaseous stream, while ash
accumulates at the bottom of the reactor, to be removed either dry or as a slag. This process must operate
as a semi-batch process with downtime to allow for the removal of char as the available equipment is not
capable of automated, continuous char removal. As this is a fixed bed gasifier, the fuel must form a
permeable bed to allow the gasification agent to flow through the reactor. These types of gasifiers are the
most developed and feature the lowest capital cost. While less expensive, these processes operate as
semi-batch, and have a lower throughput than other continuous processes.
In a fluidized bed reactor, the gasification agent is fed from the bottom of the reactor. The fuel is then fed
from somewhere near the middle of the system and proceeds to fall into the gasification agent, as can be
seen in Figure 17 below.46 As the fuel source falls into the gasification agent, it is degraded into a mixture
of volatile chemicals. As this mixture rises through the top section of the reactor it is continuously
degraded into a mixture of synthesis gas.
46
"Gasification in Detail." http://www.netl.doe.gov/. NETL: The Energy Lab, n.d. Web. 18 Apr. 2013 .
34
Figure 17. A fluidized bed reactor for the gasification of biomass
The synthesis gas then rises to the top of the reactor where a cyclone is used to separate particulate matter
from the product gas before sending it downstream. The initial breakdown of coal to a mixture of volatile
organic chemicals proceeds rapidly, effectively resulting in volatile chemicals that rise to the top of the
reactor, and char and ash which falls out through the bottom of the reactor. These fluidized bed systems
usually run at higher temperatures (800-900 oC) but lower pressures than their fixed bed counterparts.
One main disadvantage of this type of process is that a substantial amount of char is simply discarded
through the bottom of the reactor, resulting in the accumulation of a significant amount of waste.
Dual-fluidized bed reactor fall into a subset of fluidized bed reactors. As the name implies, the system
consists of two reactors. The first is a gasification chamber, where fuel is decomposed into synthesis gas,
the second is a combustion riser where char formed in the gasification chamber is combusted as seen in
Figure 18.
35
Figure 18. Operation of a dual-fluidized bed reactor
The gasification agent in this case is a hot sand which can be injected into the top of the gasifier. As the
sand falls through the reactor it provides heat for the reactions, while also trapping and removing the char
from the process. This implies that the superficial velocity of either steam or synthesis gas used to fluidize
the biomass being fed into the bottom of the reactor must be low enough to allow the sand to fall through
the reactor. In the combustion riser the char is burned away in the presence of air, reheating the sand,
allowing for the recycle of the sand. These combustion products can then be released as a flue gas, after
some purification to remove SOx and NOx, the main components of acid rain, which are released during
the char combustion. These processes are continuous and produce a large amount of synthesis gas while
also being nearly energy self-sufficient, while resulting in little net char accumulation47. For our design it
was determined that the temperature of the sand would drop by approximately 50 oC while falling through
the gasification reactor. Assuming that the enthalpy of combustion of this char is similar to that of low
grade coal, we determined that if 14% of the biomass was converted to char, this would be sufficient to
reheat the sand, and air used for combustion to the desired reactor temperature.
4.3 Process Selection
For our purposes the dual-fluidized bed reactor is the method of choice. The system operates
continuously, and self-contains and disposes of the char formed from the decomposition of biomass. The
lab of Abdelouahed48 has done significant work creating simulations of various dual-fluidized bed reactor
models in ASPEN. These systems are as follows: the TNEE process, the FERCO process and the Gussing
processes.
47
Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P., Verdier, G.; Dufour, A. Energy and Fuels,
2012, 26, 3840-3855.
48
Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P., Verdier, G.; Dufour, A. Energy and Fuels,
2012, 26, 3840-3855.
36
Table 7. Comparison of available dual fluidized bed reactor models
37
As a result, there is a fair amount of information on these process simulations in the literature. For our
purposes we have chosen the TNEE process. The low velocity fluidized bed reactor, used as the
gasification unit, allows us to use lower fluidizing agent flow rates which is advantageous for us as we
attempt to scale this process up. This process also has the most well-developed kinetic models, predicting
the synthesis gas composition within 3% of the available experimental data.
Process overview
One reason for the selection of the TNEE technology for our use was due to the availability of ASPEN
modeling data in the literature, these modeling techniques will be discussed in further detail below.
Figure 19. A simplified model for the TNEE gasification process
As seen in Figure 19. A simplified model for the TNEE gasification process, this reactor consists of
two main vessels: a main reaction unit and a secondary combustion unit. Biomass is fed into the first
reactor unit using a screw feed system, as described previously in the feed handling portion of this report.
This biomass undergoes pyrolysis, in which the biomass is degraded into a mixture of volatile organic
chemicals, which are then further modified through a set of secondary reactions. This product gas then
exits through the top of this gasification reactor which is designed as a low velocity fluidized bed reactor.
Hot sand, which provides heat for the secondary reactions and char removal, enters through the top of the
main reaction unit and falls, capturing the char, carrying it to the secondary char combustion unit. Here
char is completely combusted, reheating the sand, and results in the formation of carbon dioxide and
water. This regenerated sand is then returned to the top of the primary reactor unit to regenerate more
sand.
38
4.4 Modeling in Aspen Plus
The original simulations in the literature were performed in the following fashion49:
Figure 20. The original model of TNEE technology in Aspen, from the lab of L. Abdelouahed.
In this model, Biomass is first fed through a drier where a majority of the water is removed. This dry
biomass was then mixed with sand and a recycled stream of syngas, used to fluidize this solid feed, is
immediately fed to a pyrolysis/gasification reactor. The sand and char mixture is then separated out via a
cyclone, and the char and sand mixture is fed to the secondary char combustion unit and mixed with air.
Sand is then separated from the flue gas in a second cyclone, and the sand is mixed with the pyrolysis
product as it is sent to a secondary reaction zone. In this reactor the sand provides the energy to the
endothermic secondary reactions. The product syngas is then removed from the sand in a third cyclone,
where it is split into product and recycled streams. The sand from this cyclone is recycled to be mixed
with the dry biomass feed to the pyrolysis reactor. As this section of the report focuses on our modeling
techniques, the physical reality will be covered in a later report section.
4.5 Adapted UNISIM modeling
While this model is complete, it is not realizable in UNISM. Due to software limitations, our simulations
must be done in UNISIM, which does not contain the necessary solid operations for the modeling of this
entire system. With this in mind, the model had to be simplified. This resulted in eliminating all explicit
solid operations, that is, a biomass amount was input to a spreadsheet that calculated the pyrolysis
49
Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P., Verdier, G.; Dufour, A. Energy and Fuels, 2012, 26, 3840-3855.
39
effluent. This pyrolysis product was then modified via secondary reactions to obtain a final syngas
product. The exact modeling procedures will be described in further detail in the following sections.
4.6.1 Pyrolysis
The pyrolysis correlations give the mass fractions of pyrolysis products as a function of temperature.50 It
is assumed that this theoretical stage occurs in a negligible volume, and thus does not add volume to that
determined by the secondary reaction kinetics. This is valid as the initial gasification of the biomass
occurs rapidly, implying that the remaining reactor volume is dedicated to modifying the gaseous mixture
into the product synthesis gas. The temperature used to determine these compositions is not that of the
biomass, but that of the sand. The correlations include both the effects of biomass pyrolysis and the
primary tar conversion. The true tar is composed of over one hundred chemical species; however their
exact yields and conversions are unknown. Because of this, the literature recommends grouping tars as
benzene, phenol, toluene, and naphthalene, where each of these aromatics represents a group of similar
aromatic compounds. In addition to these tars, the pyrolysis models account for the formation of several
permanent gases, carbon monoxide, hydrogen, carbon dioxide, methane, ethylene, and ethane as well as
water. These light gases represent the main fractions of the pyrolysis product. The model takes the form
shown below:
Table 8. Parameters for the correlation of Pyrolysis
CH4
H2
CO
CO2
C2H4
C2H6
C6H6
C7H8
C6H6O
C10H8
H2O
A
B
C
-4.34E-05 1.01E-01
1.36E-05 -2.52E-02
-3.52E-05 9.77E-02
3.96E-05 -9.13E-02
-6.87E-05 1.49E-01
8.27E-06 -2.11E-02
-3.13E-05 7.54E-02
-4.54E-06 6.87E-03
1.51E-05 -3.66E-02
-8.55E-06 1.88E-02
5.16E-05 1.19E-01
-51.08
12.19
-24.93
64.02
-76.79
13.38
-42.72
1.462
22.19
-9.851
84.91
These correlations are accurate within the range of 700-1000oC, which is well within out range of
operation. Char composition and yields are not calculated from these correlations, but rather through a set
of calculations that will be discussed later.
50
Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P., Verdier, G.; Dufour, A. Energy and Fuels, 2012, 26, 3840-3855.
40
4.6.2 Secondary Reactions
The syngas and tars formed from the pyrolysis correlations then enter the secondary reaction zone.
Kinetically controlled rate laws, as seen in Table 9, are used here to consume the tars for the production
of more syngas. Physically, this portion of the main reactor unit represents the total volume, as the
pyrolysis is estimated to occur in a volume that is negligible in comparison to that of the secondary
reactions. According to our base case ASPEN simulation, heat exchange should be facilitated by the hot
sand entering the top of the reactor. This is not feasible as UNISIM is incapable of simulating
countercurrent solid flow, to account for this a key modification had to be made to the UNISIM model.
The ASPEN simulation did calculate an entering and exiting sand temperature, and these have been set as
our entering and exiting temperature to ensure our calculations match their ASPEN Simulations. This is a
fairly critical assumption to our modeling. This temperature of entering syngas was then also set as the
temperature of pyrolysis. This is due to the sand entering the pyrolysis stage as it exits the secondary
reaction stage at its lower temperature. These series of 17 reactions consume the various components
produced through the pyrolysis to produce a synthesis gas mixture. Rate laws of this form are easily input
into UNISIM, and can be used to model the secondary reactions that occur in the reactor.
41
Table 9. Secondary kinetic reactions used to modify the pyrolysis product.
42
4.7 Char Oxidation and Purity
As previously mentioned, the sand that is constantly recycled through our gasification system relies upon
the combustion of char to be reheated. In the original simulations this is included in the ASPEN models.
All previous calculations have assumed that 15% of the dry biomass weight will go to char51. According
to literature char formed over the temperature range of 700 to 1000 oC will have the composition shown
in Table 10.52
Table 10: Elemental composition of char varying with temperature of formation
Ultimate (mass fraction)
Temperature
%C
%H
%O
700 oC
91.4
2.1
6.5
800 oC
92.8
1.4
5.8
900 oC
93.9
1.1
5.0
o
94.3
1.1
4.6
1000 C
Several assumptions were made when modeling the combustion of this char. First, due to its similar
chemical composition, the char was assumed to provide a similar amount of energy as low grade coal
upon combustion (15000 kJ/kg). Second, that 15% of the anhydrous biomass is transformed into char.
51
52
Michel, R.; Gruber, R.; Burg, P.; Rapagna, S.; Mazziotti Di Celso, G.; Courson, C. Names, 2007, 99-102
Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P., Verdier, G.; Dufour, A. Energy and Fuels, 2012, 26, 3840-3855.
43
Table 11. The effect of char yield and enthalpy of combustion on sand temperature leaving the
combustion riser
%Biomass
Excess
to Char
Energy (kJ/s)
0.05
-93000
Heating
Needed
0.1
-39000
Heating
Needed
0.15
14000
Excessive
Heat
0.2
68000
Excessive
Heat
0.25
121000
Excessive
Heat
0.3
175000
Excessive
Heat
As seen in Table 11, we have determined the approximate amount of excess energy that will be obtained
from char combustion for various char amounts. In this case, excess energy is defined relative to the
amount of energy necessary to reheat the sand, and the air present for combustion, to the desired sand
temperature as it enters the gasification reactor. Negative values imply insufficient energy, while positive
values indicate that excess energy is obtained, meaning the sand is heated above the desired temperature.
If this assumption is too large, the heating from char will be excessive, which will change the dynamics of
the process, heating it until it is operating at a temperature where we may lose control. If this assumption
is too small, the reactor will slowly cool, resulting in a lessening of conversion and functionality. To
avoid this we have tabulated responses based on various amounts of biomass being converted to char. If
the char production is below a certain amount, the air being fed to combust the char must be preheated as
shown in Table 11. On the other hand if the char combustion is excessive, the air must either be diluted,
by feeding excess air to the system, to prevent overheating.
4.8 Purification of flue gas
These assumptions of our char characteristics result in the addition of flue gas cleaning equipment. Based
on the assumptions that all of the nitrogen, and sulfur of the plant material remains in the char, we find
that the flue gas has a composition of up to: 164 ppm SOx, 4210 ppm NOx in addition to releasing large
amount of carbon dioxide53.
53
"Six Common Air Pollutants." EPA.gov. EPA, 20 Apr. 2012. Web. 18 Apr. 2013.
44
Table 12. Emission limits for coal burning power plant
Chemical
Emission Limit (ppm)
Current Emissions (ppm)
NOx
8.23
4210
SOx
0.598
164
CO
7.03
unknown
As seen in Table 12, current calculated emissions are well over the minimum being released by coal
burning power plants. To ensure that our process is in compliance with these limits, we have investigated
the effectiveness of several types of flue gas cleaning equipment. Three common methods for flue gas
desulfurization are: Wet scrubbing, wet sulfuric acid process and SNOX flue gas desulfurization54,55.
Wet scrubbers: a variety of devices which remove pollutants from furnace flue gasses. In these scrubbing
towers the polluted gas is brought into contact with a scrubbing liquid in some way, allowing it to remove
pollutants.
Figure 21. The operation of a wet scrubber purifying a dirty gas
54
55
"Air Pollution Control Technology Fact Sheet." EPA.gov. EPA, 2003. Web. 18 Apr. 2013.
"Flue Gas Cleaning." Waste-to-Energy Research and Technology Council, 3 Dec. 2010. Web. 2 Apr. 2013.
45
These scrubbers have a small special requirement as they reduce the volume of the gaseous stream
through cooling. Along with being able to handle high temperatures, these have the ability to collect both
gaseous and particulate matter. While they may be very versatile, they require a large amount of power,
and tend to suffer corrosion and difficulties in product recovery. A typical scrubber system consists of
ductwork and fan system, a scrubbing vessel, an entrainment separator, pumps, spent scrubbing liquid
treatment and an exhaust stack. As product purity increases, so does the energy use56.
Wet Sulfuring Acid process: One of the key gas desulfurization processes on the market today, this
process removes sulfur from various types of process gasses in the form of commercial quality sulfuric
acid. This process is typically broken down into five sections as seen in Figure 22.
Figure 22. The major components of the Wet Sulfuric Acid process
A sulfurous feed is preheated, and enters a reactor portion where SO2 is converted to sulfuric acid. The
gas is then cooled and the acid condensed. This acid is then cooled, producing commercial grade sulfuric
acid. The first three stages produce superheated steam that can be used elsewhere in the plant. Cleaned
flue gas exits from the same stage as where the acid is condensed. This process is highly robust, being
able to handle a large variety of feed compositions. As 99% of the sulfur is recovered as concentrated
sulfuric acid, this is a very attractive economic option. While not capable of producing quantities of
sulfuric acid that justify sales, this could serve as an additional source of sulfuric acid for other onsite
processes. This process also features a simple layout and operation. While very advantageous when
removing sulfur, this system alone cannot handle a very wide range of impurities, as the wet catalyst used
is only suited for sulfur conversion. However it could be used in addition to another process to remove
sulfur in an economical way.
The SNOX process: This process is based on the Wet sulfuric acid process, but removes sulfur dioxide,
nitrogen oxides and particulate matter. The process utilizes catalytic reactions and as such does not
consume water or sorbents, and only produces dust as waste. This process consists of four main steps: (1)
Dust removal, (2) catalytic reduction of NOx through the addition of NH3, (3) catalytic oxidation of SO2
to SO3 in an oxidation reactor, and (4) condensation of Sulfuric acid to about 100 oC where it can be
56
"Air Pollution Control Technology Fact Sheet." EPA.gov. EPA, 2003. Web. 18 Apr. 2013.
46
removed as commercial grade acid57. A more detailed process flow diagram can be seen on the following
page in Figure 23.58
Due to its wide availability and robustness, we have selected the SNOX desulfurization process as our
flue gas purification process of choice. This process has the utility of the wet sulfuric acid process, as it
produced sulfuric acid which can be either used on site or elsewhere and the effectiveness of the wet
scrubbing tower, as the process can remove sulfur, nitrogen, as well as particles. The equipment for this
process is also widely commercial, as evidenced by multiple sources. Unfortunately, we were not able to
obtain quoted prices for this equipment at this time, but we believe that the operating cost of this
equipment would be minimal compared to that of the overall plant.
Figure 23 – Major components of the SNOX process
57
58
"Flue Gas Cleaning." Waste-to-Energy Research and Technology Council, 3 Dec. 2010. Web. 2 Apr. 2013.
SNOX Process. Wikipedia, n.d. Web. 9 May 2013. <http://en.wikipedia.org/wiki/SNOX_process>.
47
4.9 Optimization of process/Agreement with Reality
The original research specified several conditions which we did not attempt to re-optimize. These are as
seen in the table below:
Table 13. The set of conditions assumed from the ASPEN model
Inlet pressure
1.7 atm
Reactor volume
7.9 m3*dry biomass flow rate
Sand Flow rate
14.9 kg/s * dry biomass flow rate
The secondary kinetic reactions vary only due to temperature, so these were optimized to obtain the most
desirable ratio of hydrogen to carbon monoxide formation. Figure 24 below shows the ratio of product
formation between the main products of this gasification: carbon monoxide, hydrogen, water, and carbon
dioxide. We can see that as we raise the temperature at the exit of the reactor, the ratio of H2 to H2O
increases, in addition to the ratio of CO to H2O. This allows us to conclude that less water is formed from
our biomass, while the production of hydrogen is increased. As temperature increases we also see the
selectivity between CO and CO2 begin to drop. The optimal design temperature was discovered to be at
980 oC, where a CO:H2 ratio of 1.4 was achieved as shown in Figure 24. This ratio facilitates the
maximum production of MeOH over our catalyst in later stages of production, while not producing
excessive amounts of carbon dioxide or water. This was in agreement with the ASPEN plus model
developed by the Abdelouahed lab.
5.00000
4.50000
Production Ratio
4.00000
3.50000
3.00000
CO:CO2
2.50000
CO:H2O
2.00000
CO:H2
1.50000
H2:H2O
1.00000
0.50000
0.00000
750
800
850
900
950
1000
1050
Temperature (oC)
Figure 24: Synthesis gas production ratios
48
To maximize our synthesis gas production, it was necessary to identify a maximum size for this reactor.
Due to similarities in structure and function we decided to base this on the capacity of modern catalytic
cracking units. The largest catalytic cracking with information available to us had a volume of 1325 m3.
As such, our gasification reactor has been scaled up to this size. According to this, our gasification unit is
capable of processing 165 kg/s of dry biomass or 594,000 kg/h. At this optimized temperature the
gasification reactor produced the product ratio as seen in Figure 24.
While, this synthesis gas is not pure, it is in agreement with the experimental data we have identified in
the literature for this TNEE process. It is important to note that this kinetic model does not account for the
presence of nitrogen and sulfur containing compounds. Due to the lack of kinetic information available,
this had to be forgone. Just as with the char, we have assumed that a maximum of 75% of both the
nitrogen and sulfur could be contained in this product synthesis gas. This will be accounted for in later
purification systems.
4.10 Gasification Control Loops
Five control loops will be implemented to control the gasification reactor system. These will be
implemented so that a stable point of operation may be maintained.
1. The flow rate of the product synthesis gas will be measured, and used to manipulate the feed rate
of biomass into the system. This should grant us control over the synthesis gas production rate.
2. The Temperature of the sand leaving the combustion riser will be measured, and used to vary the
energy supplied by the air preheater (if the sand is under temperature), or the air flow rate (if the
sand is over the desired temperature).
3. The pressure of the synthesis gas that is being recycled will be measured and used to manipulate
the energy to the synthesis gas compressor. This will allow us to control the pressure of the
gasification reactor.
4. The energy that is input to the air compressor will be controlled by a pressure measurement in the
combustion riser
5. The sand flow from the gasification reactor to the combustion riser will be controlled by a weight
measurement of the sand in the gasification reactor.
5. Synthesis Gas Purification Unit
5.1 Screening Criteria
A separation process is required to make the synthesis gas suitable for processing, preventing corrosion of
equipment and destruction of catalysts. Acid gas removal (AGR) processes include “low temperature
liquid solvents, high temperature solid sorbents, membrane, and other mechanically based selective
classification systems [which include a mechanical collector with precipitator59.” The only commercial
processes currently in operation are low temperature liquid solvents in three main classes: chemical
59
Bartone, Leonard M., and Jay White. DOE/NETL, IV-12
49
absorption, physical absorption, and hybrid processes. These three main types utilize at least one
absorption column followed by a regenerator column. The columns are typically tray columns.
In a chemical absorption process, the acid gas reacts with the solvent to form an intermediate liquid
solvent species which leaves the bottom of the absorber with the rich solvent. The absorbed gases are
stripped by heating the solvent to regenerate it. Amines are commonly used as a chemical absorption
process in petroleum refining, coal gasification, and natural gas processing. In a physical absorption, the
solvent physically absorbs the acid gases without chemical reaction. Each component in the raw gas is
absorbed in proportion to its solubility and concentration, so the driving force is the high solubility of the
gases in the organic solvent. Physical absorption removes the most acid and is most economical at
conditions where solubility is highest: high pressures and low temperatures. In hybrid processes, both
chemical and physical absorption is used. The syngas exits the top of the column in both cases, and the
acid gas is removed from the solvent by indirectly heating it.
Physical absorption has three main advantages:
1) Selective H2S removal can be achieved since H2S has a higher solubility than CO2 in most organics.
The Claus recovery unit is most efficient when the feed gas contains a high concentration of H2S, so H2S
selectivity is necessary when the feed gas ratio of H2S to CO2 is low. When the H2S content is over 25%,
the waste stream can be sent straight-through Claus plants, whereas a split-flow set up and air preheating
is needed for leaner feeds.
2) Because physical solvents, such as methanol, are chemically inert, minor gas impurities like COS can
be removed without degrading the solvent from irreversible reactions.
3) Organic solvents can be regenerated by pressure reduction or inert gas stripping, which requires less
energy compared to chemical absorption.
Some of the disadvantages of physical absorption include high operating pressures and low temperatures
in order to reduce the required solvent feed to an economically viable quantity. Because of the required
high-pressure equipment and additional heat exchange and refrigeration equipment, the capital and
operating costs are typically higher than the amine process.
The following criteria were assessed when selecting an acid removal process:




Required product purity- According to NREL, the catalyst used in the methanol reactor can
tolerate an H2S concentration up to 20 ppm to maintain activity and can tolerate a CO2
concentration up to 5 mol%.
Simplicity of the process design- We must be able to model the design using UNISIM. The
process should not have any unnecessary complexity that will add to the cost of the process.
Chemical compatibility preventing solvent degradation- Impurities in the syngas such as HCN,
COS, and mercaptans must not result in irreversible reactions with the solvent that would cause
solvent loss or degradation. Chemical absorption processes such as MEA may not be suitable
because of these irreversible reactions.
Commercial experience- The gas removal process should be commercially available and proven
feasible through implementation on an industrial-scale.
50



Control and selectivity of H2S vs. CO2- The process should have a high degree of control with
meeting the cleaning specification, so it should be able to first remove H2S, then CO2 from the
feed stream. “Primary and secondary amines are known for their non-selectivity” (NREL, 16),
while physical solvent systems can selectively remove H2S from CO2.
Minimum loss of product gases- If a significant amount of CO is absorbed during the separation
process, the yield of the final alcohol product will be lower and the plant efficiency will be
reduced. Thus, the process should minimize the absorption of CO and hydrocarbons both to
increase plant efficiency but also to prevent the environmental impacts of CO and hydrocarbon
gases in the waste stream.
Minimal cost- The acid gas removal should minimize not only utility and capital costs of the
separation process, but should ultimately minimize the economics of the integrated process.
5.2 Purification Alternatives
5.2.1 The Rectisol Process
Licensed by LindeAG and Lurgi AG, the Rectisol process is an acid gas removal process that separates
mainly hydrogen sulfide and carbon dioxide using methanol as a solvent. Trace contaminants such as
ammonia, mercury, and hydrogen cyanide are also removed. Cold methanol at approximately -60 °F
absorbs the acid gases at a pressure between 400 to 1000 psia. The pressure is then reduced to release and
recover the gases. The hydrogen sulfide is sent to a Claus unit, an industry standard patented by Carl
Friedrich Claus, for conversion to elemental sulfur. A typical flow process is pictured in Figure 25.
“Rectisol processes have been operated reliably for decades behind many types of gasification
processes… There are about 59 gasification facilities world-wide… that have been operating
Rectisol processes, and about seventy-five percent of the world’s syngas produced form oil
residue, coal, and wastes is purified by the Rectisol process (DOE/NETL, IV-14).”
51
Figure 25. Typical Rectisol Process Flow Scheme
5.2.2 The Selexol Process
The Selexol process is a well-known and widely used process that uses dimethyl ether (DME) of
polyethylene glycol as the solvent. It has been used for natural gas processing, ammonia and hydrogen
production, and petroleum and coal gasification60. The solvent has chemical and thermal stability, a
favorable solubility for acid gases, and a low vapor pressure, minimizing solvent losses. Figure 26
illustrates the Selexol process. The feed gas enters the absorber at a high pressure and low temperature so
that the acid gases are absorbed by the solvent. The solvent is regenerated by pressure reduction through
flash drums followed by thermal regeneration. Before returning to the top of the absorber, the lean
solution is either cooled or chilled.
60
Kohl, A.L., and Nielsen
52
Figure 26. Typical Selexol Process Flow Scheme61
However, the BTX that is contained in the synthesis feed gas would dissolve in the Selexol solvent and
come out with the acid gas, making it unacceptable for the Claus unit. The paraffins and olefins would
mostly dissolve in the solvent as well, contaminating the acid gas for the Claus unit. Selexol will not
remove COS from the raw gas, so a COS Hydrolysis Unit would be required, adding complexity and
increased costs.
5.2.3 Amine Processes
The three basic amines commercially available include primary amines like MEA or DGA, secondary
amines like DEA or DIPA, and tertiary amines like MDEA. Primary and secondary amines react as
follows:
Selective recovery of H2S and CO2 is difficult because most often these reactions occur simultaneously.
The cost of an amine process depends largely on energy requirements of the solvent regeneration and “the
solution circulation rate, which is based on the solution capacity and concentration62.”
A typical amine process is shown in Figure 27. A knock-out drum removes any condensate from the feed
gas, which then enters an absorber and reacts with the amine solution flowing down the top. The sweet
gas exits the top, and the amine solution exits the bottom. A flash drum is used to remove from the amine
61
62
W. Breckenridge, A. Holiday, J. Ong and C. Sharp
"Survey and Down-Selection of Acid Gas Removal Systems.” National Renewable Energy Laboratory, 21
53
any dissolved hydrocarbons, which are then used as plant fuel. The amine solution is heated before
entering the stripper where acid gases are removed. Water is recovered from the stripped acid gas by
cooling the gas before sending it to a sulfur recovery unit. The amine solution exiting the stripper is
cooled before being recycled to the top of the absorber.
Figure 27. Typical Amine Process Flow Diagram63
MDEA has been successfully used to treat coal-derived gases, but none of the amine processes have been
demonstrated on a commercial scale with biomass-derived syngas. The Ucarsol Process is a chemical
process currently marketed by the Dow Chemical company that uses MDEA as the solvent (DOE/NETL,
IV-18). However, this process will not remove COS from the gas stream, so a COS Hydrolysis Unit
would be required. This unit converts the COS and CS2 into H2S, which is then removed by the amine
unit. The process can remove H2S to about 5 ppm. A three-phase separation system could separate the
BTX as a separate liquid stream. Corrosion is an important consideration because H2S dissociates in water
to form a weak acid, so normally the process equipment is clad with stainless steel.
5.2.4 The Hybrid Processes
Hybrid processes take advantage of both a chemical solvent and a physical solvent by combining the two
processes. Hybrid processes treat gases containing COS more effectively than amine systems. Solvents
generally have an even lower solubility for hydrocarbons than physical absorption systems, allowing
more efficient alcohol production. However, the solvent loading is higher than chemical solvents. A
common solvent is a mixture of tetrahydrothiophene dioxide, an alkanolamine (either DIPA or MDEA),
and water, used for Shell’s Sulfinol process. The costs associated with high operating pressures and
higher solvent costs make hybrid processes generally more expensive. Figure 28 shows a typical hybrid
63
Engineering Data Book
54
process. The FLEXSORB SE process is a hybrid physical/chemical AGR marketed by the ExxonMobil
Company that uses hindered amines (IV-19, DOE/NETL). The process can remove H2S to about 20 ppm
in the synthesis gas, but it would require an additional COS Hydrolysis Unit to remove COS.
Figure 28. Typical Hybrid Process64
5.3 Purification Process Selection
The Rectisol Process was chosen from the separation alternatives because it has the ability to remove H2S
and COS to sulfur concentration criteria and to reduce the CO2 concentration without destroying the
solvent. This process is widely used on an industrial scale and has been proven to be economical.
Methanol as a solvent is inexpensive compared to the Selexol solvents and it requires less steam energy
for regeneration. Although the capital costs are higher to maintain the low temperatures and high
pressures, the purity of the cleaned gas is higher because methanol as a physical solvent can remove
greater percentages of acid gas components. The process is flexible and can be suited to separate the gas
into various components based on the final product desired. The decision is supported by the decision
matrix shown below. The weight column indicates the relative importance of the given design criteria,
with 1 being least important and 10 being most important. Each separation process was given a number,
with a weak score of 1 and a strong score of 10 in each design category. The score for each category was
then multiplied by the weight and summed for each separation process. Rectisol was chosen for the
highest score of 296.
64
Li Sun, Robin Smith
55
Table 14. Purification Process Decision Matrix
Criteria
Weight
Rectisol
Selexol
Amine
Hybrid
Required Product
Purity
Process Design
Simplicity
Minimum Solvent
Degradation
Commercial
Experience
Selectivity of H2S vs.
CO2
Minimum Loss of
Product
Minimal Cost
Total Score
2
10
9
8
2
9
8
9
5
4
7
8
8
4
6
4
8
7
4
4
5
8
6
2
4
8
4
2
8
6
8
7
296
8
293
8
243
4
198
Process Product Purity: Rectisol is capable of reducing the acid gas to 0.1 ppm H2S while Selexol can
reduce it to 1 ppm. Amine processes typically reduce H2S to between 5-10 ppm, and the hybrid processes
reduce the H2S to about 20 ppm (NREL, 36). The hybrid processes were given a low score because they
are on the boundary of the 10-20 ppm H2S content requirement. Because all of the processes will bring
the acid gas to the required H2S content, the weight of this category is low.
Process Simplicity: The Rectisol is slightly more complicated than the Selexol process because
refrigeration is required. The amine and hybrid processes were given a low score because simulating the
reactions taking place in the absorber would be much more complicated and outside the scope of our
knowledge and experience. For this same reason, the weight of this category is large.
Minimum Solvent Degradation: The amine and hybrid processes were given a lower score than the
physical absorption processes because the amine solvent reacts with impurities like COS.
Commercial Experience: All of the processes are commercially available on an industrial scale, so the
weight of this category is low. Amine solvents have not been used with biomass-derived syngas, so these
processes received a lower score.
Selectivity of H2S vs. CO2: Physical absorption processes have this capability while chemical absorption
processes do not because of simultaneous reactions. Selexol would require an additional absorber to
remove CO2, so its score is slightly lower than Rectisol.
56
Minimum Loss of Product: “Absorption of CO and hydrocarbons is expected to be greater for physical
absorption than chemical absorption due to the higher solubility of these compounds in a physical
solvent” (NREL, 17). Thus, the physical processes were given a lower score. Selexol was given a slightly
lower score than Rectisol because hydrocarbon losses can be significant when treating hydrocarbon-rich
gas streams.
Minimal Cost: The hybrid processes would be most expensive because of royalty costs and higher solvent
costs. It is difficult to determine the costs associated with the other three processes without doing a
complete analysis. Selexol solvent is more expensive, but does not have the high cost of refrigeration as
of Rectisol. The physical solvents can be regenerated by pressure reduction, inert gas stripping, or thermal
regeneration, which require little energy compared to that of chemical due to the lower heat of desorption
of acid gases. Amine solvents are more expensive to input and regenerate but don’t have cost of high
pressure and temperature.
5.4 Rectisol Process Description and Simulation
A summary of our design of the Rectisol process is depicted in Figure 29. The workbook from our
modeling of this process in UNISIM is copied in Appendix H. Approximately 1.377*106 lb/hr of raw gas
from the syngas coolers enters an H2S absorber where H2S, COS, HCN, and NH3 is removed. Most of the
methanol is recovered in a hot regenerator and methanol stripper. About 64 lb/hr of fresh methanol is
required. The raw gas is first washed with methanol in the bottom section of the absorber before entering
the main section by a chimney tray. The laden methanol leaving the bottom of the absorber enters a sour
flash; the sour flash generates sour gas sent to the Claus plant and liquid sent to the methanol stripper.
The laden methanol that leaves the main section of the absorber is fed to a flash regenerator, along with
tail gas from the Claus unit. The gas that leaves the regenerator is directed to the hydrogenation reactor in
the Claus unit. The methanol leaving the bottom undergoes a final regeneration by stripping with
methanol which has been vaporized via low pressure stream in a reboiler. After the exiting acid gas is
cooled, some of the gas is recycled back to the regenerator while the rest is transported to the Claus unit
for sulfur recovery. A refrigeration system requiring about 34,000 kW of electrical power supplies the
refrigerant for cooling the various streams. Pumping power requirements total about 517.3 hp. About
1.307*106 lb/hr of clean syngas is produced, resulting in an efficiency of about 95%. About 600 lb/hr of
elemental sulfur is produced in the Claus unit, which is further described below. The H2S content is
reduced from approximately 260 ppm in the feed to about 15 ppm in the exiting gas.
57
Figure 29. Rectisol Process Flow Diagram
58
We did not model the Claus process, but it is an industry standard with available data and cost
estimations. The process recovers sulfur from the acid gases composed mainly of hydrogen sulfide. The
overall main reaction is:
2 H2S + O2 → S2 + 2 H2O
(1)
According to the 2006 United States Geological Survey, the majority of the 64,000,000 metric tons of
sulfur produced worldwide was byproduct sulfur from refineries and other hydrocarbon processing
plants65. Sulfur is used for manufacturing medicine, cosmetics, sulfuric acid, fertilizers, pesticides, and
rubber products. The process involves a Claus furnace and two to three catalytic stages which recover
about 99% of the sulfur from the acid and sour gases from the Rectisol process. The feed streams are
preheated with steam before entering the furnace, where H2S is reacted to SO2. The exhaust is sent to a
boiler to generate the steam. In the catalytic stages, the SO2 and any remaining H2S in the exhaust is then
reacted to sulfur and water, typically with an activated aluminum(III) or titanium oxide(IV). Tail gas is
recycled to the Rectisol process while the sulfur is condensed and collected in a sulfur pit to be
transported to a sulfur storage area. Figure 30 shows the flow scheme.
Figure 30. Claus Sulfur Recovery Flow Scheme66
The economics for the Claus recovery unit were estimated based on the sulfur recovery stage economics
presented by a “techno-economic analysis of biomass-to-liquids production based on gasification model”
completed by graduates at the Iowa State University. The gas flow rates of the model are on the same
scale as our model. The total installed cost in 2007 is estimated at $9.9 million67, so the projected cost is
scaled to a total of $11.4 million.
65
“Sulfur”
Mbeychok
67
Swanson, Ryan Michael
66
59
5.5 Optimization
When optimizing the separation process, tradeoffs had to be made. In order to lower the amount of
methanol needed in the absorber from both the fresh feed and recycle feed, the pressure in the absorber
needed to be increased, so that the H2S content would still remain within limits. The tower pressure was
increased from 330 psia to 520 psia, still within the suggested range of 330-1000 psia68. This pressure
allowed the methanol recycle to be reduced from 800,000 lbmol/hr to 172,300 lbmol/hr while the
methanol fresh feed was reduced from 50,000 lbmol/hr to 2 lbmol/hr, allowing the methanol input to be
economically feasible. Figure 31 illustrates how the H2S in the gas exiting the absorber drops significantly
at 520 psia. At 450 psia or less, the H2S content does not meet the catalyst specifications.
24
H2S in exit gas (ppm)
22
20
18
16
14
12
10
400
420
440
460
480
500
520
540
Pressure in absorber (psia)
Figure 31. Optimizing pressure in the absorber with an input of 2 lbmol/hr of fresh methanol
At a pressure of 520 psia, increasing the fresh methanol input does not decrease the H2S content of the
exiting gas significantly, as shown in Figure 32 where the H2S does not change by more than 0.01 ppm
with a ten-fold increase in methanol input.
68
“Process Screening Analysis of Alternative Gas Treating and Sulfur Removal for Gasification”
60
15.538
H2S in exit gas (ppm)
15.537
15.536
15.535
15.534
15.533
15.532
15.531
15.53
15.529
15.528
0
5
10
15
20
Fresh Methanol Feed (lbmol/hr)
Figure 32. Increasing the fresh methanol to the absorber does not decrease the H2S content of the exit gas
substantially
The H2S removal improved as more trays were added to the absorber up until 20 trays, as shown in
Figure 33. After 20 trays, the H2S removal did not improve significantly.
24
H2S in exit gas (ppm)
22
20
18
16
14
12
10
10
12
14
16
18
20
22
24
# of trays in absorber
Figure 33. Optimizing number of trays in the absorber
The most H2S was removed when the treated methanol entered tray 17 and the side draw exited tray 17,
near the bottom of the absorber. The temperature of the methanol feed to the sour flash needed to be set at
210°F or higher in order for any of the methanol to separate from the H2S. The heat exchanger between
the absorber bottoms and the treated methanol was designed to achieve the maximum cross temperature.
The heat exchanger requires 10 shells in series and one tube pass per shell with one counter-flow shell
61
pass and only one shell in parallel. Even with this design, another heat exchanger is required to reduce the
temperature of the treated methanol from -8.7°F to -60°F and a heat exchanger to increase the temperature
of the absorber bottoms from 179°F to 210°F.The heat exchanger between the raw syngas and the treated
syngas has a similar design with 10 shells in series and one tube pass per shell with one counter-flow shell
pass and one shell in parallel. As shown in Figure 34, the amount of H2S removed does not change much
when the syngas is cooled past the minimum temperature, 170°F, achieved in the heat exchanger, so a
second heat exchanger is not needed before the syngas is fed to the absorber.
15.55
H2S in exit gas (ppm)
15.5
15.45
15.4
15.35
15.3
15.25
15.2
-100
-50
0
50
100
150
200
Syngas Feed Temperature to the Absorber (°F)
Figure 34. The syngas entering the absorber does not need to be cooled past 170°F since the H2S content
does not change rapidly with temperature of the feed gas
UNISIM simulations showed that the required purity of the exiting syngas is achieved when the methanol
stripper removes at least 90% of the H2S from the incoming methanol before it is recycled to the absorber.
The separation improves when the pressure is decreased, as shown in Figure 35, so a pressure of
approximately 60 psia is the highest pressure that will allow 90% removal. Thus, the methanol stripper
pressure was set just below that at 55 psia.
62
% Removal of H2S in Methanol Stripper
1
0.95
0.9
0.85
0.8
0.75
0.7
y = -0.004x + 1.1361
R² = 0.9966
0.65
0.6
40
50
60
70
80
90
100
110
Pressure of the Methanol Stripper (psia)
Figure 35. 90% removal of H2S entering the methanol stripper is required to achieve desired syngas
purity
5.6 Vessel Specifications
Sizing and material specifications are listed in Appendix F. The size was determined using suggested
parameters for the Rectisol process. These values were confirmed by first calculating the abscissa ratio
from the liquid and vapor flow rates, densities, and molecular weights provided by UNISIM. From this
ratio, Figure 6.23 of Transport Processes and Separations69 was then used to find the CF in order to
calculate a capacity parameter using Equation 6-42. After using the Oliver correlation, a diameter could
be calculated using Equation 6-44. Carbon steel was specified for most of the vessels, but 316 stainless
steel was indicated for vessels that are more susceptible to corrosion with water and H2S. Stainless steel
contains more chromium than carbon steel, so it can form a passive film which prevents rusting and
flaking from spreading internally.
5.7 Control and Safety
The presence of hydrogen sulfide gas is a safety concern for the Rectisol process. Hydrogen sulfide is a
toxic and irritating gas which can cause fatalities without the proper training and monitoring equipment.
Deadly exposures to employees must be prevented by early recognition and detection. For employees
working in an area with the potential to contain hydrogen sulfide, training must include learning how to
recognize the signs and symptoms of exposure, how to monitor for hydrogen sulfide, and how to take
protective measures. Instrumentation must continuously monitor the atmosphere, particularly in confined
spaces, for hydrogen sulfide and oxygen deficiencies.
69
Geankoplis, Christie J.
63
According to OSHA, hydrogen sulfide “has a permissible exposure limit of 20 ppm ceiling concentration
and a peak exposure limit of 50 ppm for no more than 10 minutes70.” Inhaling low concentrations can
cause headache, dizziness, and upset stomach. A strong odor of rotten eggs at low concentrations and a
sweetish odor at higher concentrations serve as a warning signal. At concentrations higher than 30 ppm,
hydrogen sulfide may deaden the sense of smell. If concentrations of 500-1000 ppm are inhaled, rapid
unconsciousness and death through respiratory paralysis will most likely result. Respiratory protection
such as the supplied airline respirator should be used as a backup to the engineering controls implemented
in the plant.
At the highest level of plant-wide control, corporate management uses market forecasts, raw material, and
product prices to make production decisions. On the next level, operating conditions for each process unit,
such as the syngas purification stage, in the plant are decided based on these current and future gasoline
production goals. Setpoints for each unit operation, such as the absorber, are determined based on product
quality and temperature. Finally, at the unit operations level, process flow rates are set, and each
controller determines, for example, the pressure to the control valves.
Typical equipment control schemes for towers and heat exchangers are depicted in Figure 36 and Figure
37 respectively. As shown in Figure 36, the feedstream is under flow control. Three controlled outputs
result from the two-product column: pressure, distillate receiver level, and bottoms level. Five
manipulated inputs include cooling water flow, distillate product flow, bottoms flow, reflux flow, and
steam-to-reboiler. Because the bottoms product of the hot regenerator is recycled back to the absorber
after entering the methanol stripper, the composition must meet a hydrogen sulfide limit. Thus, a dual
composition specification, where overhead and bottoms composition are measured and controlled, is
assumed for the control strategy. There are no degrees of freedom since each of the measured outputs are
paired with a manipulated input. The bottoms composition controller manipulates the steam input while
the level controller of the tower manipulates the flow rate of the bottoms product. The pressure controller
manipulates the amount of cooling water used in the distillate receiver while the reflux composition
controller manipulates the reflux flowrate. Finally, the level controller of the distillate receiver
manipulates the distillate product.
70
Kalusche, Hilton E.
64
Figure 36. Control of Hot Regenerator; typical tower control71
As shown in Figure 37, the preferred method of the heat exchangers is a cold stream by-pass. This allows
an effect that is almost immediate on the cold stream outlet temperature. The temperature controller
manipulates the amount of the cold stream that is bypassed.
71
Bequette, Wayne B.
65
Figure 37. Manipulation of Cold Stream By-pass
6. Production Reactor System
After the synthesis gas was sufficiently cleaned, the purified synthesis gas enters a production reactor
unit, composed of two reactor systems in series, separated by a flash drum. Figure 38 displays a process
flow diagram of the proposed production reactor unit.
66
Process Flow Diagram—MeOH to Product Reactions
38
40
K-400
43
36
Purge
37
E-400
42
39
41
R-400
44
E-401
17
V-400
45
To Final Separations
47
46
48
Z-400
E-402
R-500
Figure 38. Production System Schematic as a PFD.
67
Purified synthesis gas leaving the Rectisol process enters the unit at 919oC and 2000 kPa, and is mixed
with a cool recycle to a temperature of 528oC. The stream is subsequentially cooled to 400oC and a
pressure of 2000 kPa. The gas stream enters a horizontal heat exchange PBR and upon leaving the
reactor, is sent to a flash column, operating at 60oC, to separate the methanol from unreacted product. The
unreacted product stream is split, sending 40% of the stream to a purge and the remaining 60% to be
recycled back to the reactor. This reactor achieves 62% H2 conversion and produces 41 kg/s of methanol.
The liquid stream leaving the flash column is passed over a pressure relief valve, dropping the pressure
from 1225kPa to 110kPa. The liquid stream, now at nearly atmospheric pressure, is reheated to a
temperature of 670oC, and sent through a second heat exchange reactor, where the methanol is
catalytically converted to gasoline products.
6.1 Selection of a Product
Dynamic Organics also considered several chemical substances as product material. Some of the
alternatives considered included methanol, dimethyl ether (DME), alcohols such as ethanol or butanol, as
well as many other alkane derivatives. The type of product depended largely on the process, catalysts
available and the economics.
When Cornell University did a study in 200572 on the effects of using a butanol-based fuel in the engine
of automobiles, some interesting results were discovered.
Butanol is a C4 molecule whereas ethanol has two C atoms. Table 15 illustrates the way in which
butanol’s larger molecule translates into more energy: 110,000 BTU/gallon versus the 78,000 BTU/gallon
associated with ethanol. Table 15 also illustrates that butanol is safer to use than both ethanol and
gasoline as a result of its lower vapor pressure—it is difficult to ignite and it burns slowly. Like diesel
fuel, a match must be held to it for ignition; butanol is combustible but not flammable, whereas ethanol
and gasoline are flammable and potentially explosive. With this in mind, a butanol-based engine may be
more difficult to start on cold mornings, and therefore, the amount of butanol able to be mixed with
gasoline is limited. However there is a flip-side to this issue as well: The fuel in an engine has to be
vaporized before it will burn. As the heat of vaporization of butanol is less than half of that of ethanol73,
an engine running on butanol should be easier to start in cold weather than one running on ethanol or
methanol.
Table 15. Properties of Fuel-Grade Alcohols and Gasoline
Energy Content
(BTU/gallon)
72
73
Ethanol
Butanol
C2H5OH
C4H9OH
78 k
110 k
Gasoline
115 k
Ramey, David E. "Butanol: the Other Alternative Fuel."
J.L. Smith; J.P. Workman (December 20, 2007). "Alcohol for Motor Fuels". Colorado State University.
68
Motor Octane
92
94
9474
Air:Fuel Ratio
9
11-12
12-15
Vapor Pressure
(psi@100⁰F)
2
0.33
4.5
When Cornell University considered that an average car gets approximately 22 miles to the gallon using
gasoline, and assumed a modest estimate for the cost of gasoline at $3.00/gallon, the results of this study
are presented in Table 16.
Table 16: Costs per mile (2005) for E85 (Ethanolic Gasoline), Unleaded Gasoline and Butanol
Cost Per
Mile
Cost/gallon
Average
mpg
E85
$2.80
Gasoline
$3.00
Butanol
$3.00
E85
17.6*
$0.16
Gasoline
22
$0.14
Butanol
25
$0.12
*20% less than for Gasoline
With the same cost, yet slightly lower energy, the lower cost per mile for butanol is surprising. However,
when you take into consideration that butanol has the lower vapor pressure and burns more slowly, more
miles per gallon is able to be taken from butanol.
Because this study was done in 2005, the numbers for fuel cost have fluctuated from the values displayed
in the table, however, the lower cost per mile associated with butanol is highly encouraging. It suggests
that regardless of the way in which the numbers fluctuated, butanol would still result in a more financially
reasonable option.
Another important consideration for choosing a product fuel is the azeotropic data for various fuel options
with water. Azeotropic mixtures cannot be easily separated by ordinary distillation methods, and in some
cases, like that of ethanol and water, this creates a purity restriction and significantly increases the cost of
the equipment necessary to separate the mixture. Gasoline cannot tolerate the water in azeotropic ethanol,
and therefore, extensive measures would be required to separate the water from ethanolic fuel. The
azeotropic data for ethanol and butanol with water can be found below.
74
Helmenstine, Anne Marie.
69
Table 17: Azeotropic Data for common Fuel Alcohols with Water, b.p.=100⁰C75
Boiling point of
Component (⁰C)
Boiling point of
Mixture (⁰C)
% by weight
Ethanol
78.4
78.1
95.5
1-Butanol
No Azeotrope with Water
Clearly, the use of butanol as a fuel appears to be a more practical option than the use of ethanol. The
main reason for this thinking is that if butanol is produced, the separation step, regardless of the process
used, would be significantly simpler because the separation would not have to be made around the
azeotrope.
At the end of the fall semester, the product fuel still was not able to be determined. The reason for this
was that while the overall process (synthesis gas) and feedstock (Miscanthus Giganteus) was more or less
established for the project, unknowns, such as the cost of the catalyst, which had not been answered
prevented any definite decision making. Butanol appeared to be a much more feasible option than
ethanol, given the data presented above. However ultimately, the final product depended on what process
would be best to model (in terms of yield and necessary separations) and yield the most economically
beneficial product to produce.
In the spring semester, research continued into the product options. Hydrocarbons, long chain alcohols,
dimethyl ether (DME) and a mixture of gasoline blending components were each examined for their
relevance and alignment to our process.
With each alternative fuel model under consideration, the idea of cultural appropriateness became an
important consideration. The current alternative fuel options on the market (reference) require
modification of car engines in order to use. We hoped to produce a fuel that would be readily available,
and easy for someone to use without requiring major modifications to the vehicle. We had determined
that if modification to a vehicle was necessary, the fuel would never be used to the extent that would be
required to make a significant impact. With this in mind, we began to consider fuels with properties very
similar to those of gasoline.
6.2 Process Alternatives
Many common industrial processes exist which convert synthesis gas to a useable product. In this case,
the team’s desired product was a fuel—alcohols, hydrocarbons, and the like. Three common industrial
routes were examined to determine which would be the best path, resulting in the most efficient and costeffective solution.
75
Azeotrope Database. The University of Edinburgh, n.d. Web. 19 Nov. 2012.
<http://www.chemeng.ed.ac.uk/people/jack/azeotrope/EE.html#ethan>.
70
6.2.1 Fischer-Tropsch Synthesis
The first path was a synthesis gas to hydrocarbon reaction process via Fischer-Tropsch synthesis. In the
Fischer-Tropsch (F-T) process, synthesis gas, or a mixture composed of mostly hydrogen and carbon
monoxide, obtained from coal, biomass, or natural gas is converted to a multi-component mixture of
hydrocarbons. Fuels produced from the FT Process are of high quality due to very low aromaticity and the
absence of sulfur-containing compounds. However, the yield of hydrocarbons within the range of general
gasoline is low, but rather, FT produces a fuel similar to diesel76, which can also be used in automobiles.
As the scope of our project concerned the United States and diesel fuel is used most often for larger
industrial vehicles in the US, it was not the ideal choice for our project.
Many types of reactor have also been considered during the FTS development. These include a fixed bed
reactor, a fluidized bed, and a slurry reactor. The fixed-bed reactor has been most successful in industry,
being used by large scale operations such as the ones implemented by Shell and Sasol.
To achieve optimum performance, both the catalyst and the reactor must be optimized together. Due to
the complexity of the system, it is necessary to first gather all the required kinetic data. The kinetic data
for the FT synthesis has been studied extensively to determine an appropriate rate law for the involved
kinetics.
Based on the work of Marvast, Sohrabi and Ganji77, who examined a packed-bed reactor system and
assumed a one-dimensional modeling for the reactor. In their analysis, they assumed that the reactor was
operating at steady state; the model was pseudo-homogenous, meaning that there is no concentration or
temperature gradients in the catalyst pellet. They also assumed that there is no concentration or
temperature change in the reactor radial direction, and that there is no gas radial velocity. Radial and axial
dispersion were ignored, and the reactor was assumed to be isothermal.
Table 18 displays the reactions for producing main components in their kinetic study.
Table 18. Fischer-Tropsch Primary Reactions.
CO + 3H2  CH4 + H2O
(2)
2CO + 4H2  C2H4 + 2H2O
(3)
2CO + 5H2  C2H6 + 2H2O
(4)
3CO + 7H2  C3H8 + 3H2O
(5)
4CO + 9H2  n-C4H10 + 4H2O
(6)
4CO + 9H2  i-C4H10 + 4H2O
(7)
6.05CO + 12.23H2  C6.05H12.36(C5 ) + 6.05H2O
(8)
CO + H2O  CO2 + H2
(9)
+
76
"Alternative Fuels Data Center: xTL Fuels." US Department of Energy
Marvast, M. Ahmadi, M. Sohrabi, and H. Ganji. "Kinetic Modeling of the Fischer-Tropsch Reactions and
Modeling Steady State Heterogeneous Reactor."
77
71
The rate laws resulting from Marvast, Sohrabi and Ganji’s study were presented as functions of the partial
pressures of hydrogen and carbon dioxide. Their work presents a simple expression for components’
effectiveness factor as a function of partial pressures and temperatures in the gas phase. A total of 26
models were tested, and a kinetic model was determined to accurately represent all data.
6.2.2 Dimethyl Ether Synthesis
A second alternative considered involved synthesis to Dimethyl Ether (DME). DME has emerged in
recent years as a clean alternative fuel for diesel. Via the work of Shim et al78 in Suwon, Korea, a direct
method for producing DME was optimized by examining conversion and production rate of DME. Using
a kinetic model developed in the paper, a simulation model was developed in the ASPEN Plus simulator.
The syngas coming from a gasification process contains mostly carbon monoxide and hydrogen. Overall
DME synthesis reactions consist of three steps: methanol synthesis, dehydration and water gas shift
(WGS) reactions. The formation of methanol is considered to take place via two reaction mechanisms;
reaction (10) is the formation of methanol using H2 and CO as major reactants. This reaction has high
heat of reaction. While reaction (11) is promoted by CO2 and H2 as reactants, it has significantly less heat
of reaction than reaction (10). In the work done by Shim et al, reactions (10) and (11) are assumed to
produce methanol simultaneously.
CO + 2H2  CH3OH
CO2 + 3H2  CH3OH + H2O
-43.4 kcal/mol-DME
-13.5 kcal/mol-DME
(10)
(11)
Methanol passes through a dehydration process with a DME synthesis catalyst, Cu/ZnO/Al2O3, and
proceeds in terms of reactions (12) and (13):
2CH3OH  CH3OCH3 + CO2
-5.6 kcal/mol-DME
(12)
CO + H2O  CO2 + H2
-9.8 kcal/mol-DME
(13)
The kinetics of these reactions has been studied in detail. Because the DME synthesis is governed by
catalytic reactions of syngas on the surface of the catalyst, it is assumed that each CO and CO2 is
comparatively adsorbed at an active site on the catalyst surface and that each H2 and H2O is also
comparatively adsorbed at the other. It is suggested that both reactions follow the LangmuirHinshelwood-Hougen-Watson (LHHW) model. This model suggests that both molecules adsorb to the
catalyst surface, and that both adsorbed molecules undergo a bimolecular reaction.
6.2.3 Gasoline Products via Methanol
Finally, the third synthetic approach considered in this analysis is the production of larger hydrocarbons,
possessing similar properties to the components found in gasoline, via methanol. Methanol is very
commonly used as a feedstock in the chemical industry. It is produced under high pressure and
temperature from synthesis gas (a mixture of CO/CO2/H2).The use of CO2 as a feedstock in methanol
synthesis has gained a lot of attention and is now widely studied.
78
Shim, Hyun Min, Seung Jong Lee, Young Don Yoo, Yong Seung Yun, and Hyung Taek Kim. "Simulation of
DME synthesis from coal syngas by kinetics model."
72
In the work of Panahi and Mousavi79, CO2 is used as the base component. In methanol synthesis, either
CO or CO2 or both hydrogenate to methanol. The reactions, which take place together in one reactor, are
represented in equations (14) and (15).
CO2 + 3H2  CH3OH + H2O
CO2 + H2  CO + H2O
-49.43 kJ/mol
41.12 kJ/mol
(14)
(15)
In a second reactor, methanol is converted to gasoline range hydrocarbons over modified HZSM-5 (often
referred to as “zeolite”) catalyst. An oxalic acid-treated ZnO/CuO/HZSM-5 catalyst was prepared for the
production of gasoline range hydrocarbons from methanol. Previous research80,81,82 studied the effect of
CuO and ZnO loading on HZSM-5 catalyst for the conversion of methanol to hydrocarbons. The results
of these studies indicated that 0.5wt% ZnO/7wt%CuO/HZSM-5 had higher conversion and hydrocarbon
yields as compared to other catalyst. The high surface area of the zeolite catalysts used in the reaction
allows for a high degree of dispersion of active metals over the zeolites, making for maximum use of the
deposited metals. In the paper by Zaidi and Pant, the authors claim olefins are the primary products and
proposes that autocatalytic steps between oxygenates and olefins are important to the model in addition to
a bimolecular step accounting for the carbene insertion in the primary olefins.
The production of several hydrocarbons includes methane, C2 to C5+ selectively linear molecules,
dimethyl ether, and aromatic rings ranging from C6 to C10. The smaller hydrocarbons will be sent to a
furnace to be burned for heat to be used in other processes.
6.2.4 Process Selection
Three main factors were taken into consideration when deciding on a process for product formation. The
first consideration was single pass yields of product. Because of the large scale of the processes under
consideration, even small changes in the overall yield of the reactions causes a significant change in the
amount of product. The second consideration taken into account was the complexity of the end
separations. In many processes which utilize synthesis gas to create a fuel, hydrocarbons or alcohols,
azeotropes between water and other components exist which can quickly complicate separations which
otherwise would be fairly simple. Efforts were made to avoid azeotropic mixtures in order to simplify the
final step in the reaction process. Finally, the cultural appropriateness (that the fuel could be used with
little or no modification to current automobile engines) was taken into consideration.
Each of the three approaches was modeled in a PFR reactor in UNISIM. The most successful synthetic
approach was the production of large aromatic hydrocarbons, given by scenario three: gasoline synthesis
via methanol. Because more methanol was made in one pass through the reactor using this kinetic data,
and the formation of methanol led to several more options for products, including large hydrocarbons,
alcohols, and some specialty chemicals, scenario three was chosen to move forward.
6.3 Synthesis Gas to Methanol Reactor
79
Panahi, Parvaneh Nakhostin, Seyed Mahdi Mousavi, Aligholi Niaei, Ali Farzi, and Dariush Salari.
H. A. Zaidi and K. K. Pant, Catal. Today, 96, 155 (2004).
81
H.A. Zaidi and K.K. Pant, Korean J. Chem. Eng., 22, 353 (2005).
82
H.A. Zaidi and K.K. Pant, Ind. Eng. Chem. Res., 47, 2970 (2008).
80
73
Optimization of the synthesis gas to methanol reactor was completed in three stages, with each stage
eliminating a previously made assumption. In the first stage, the simulation assumed an isothermal reactor
design by forcing the feed and effluent streams to the same temperature. In this stage, reactor sizing was
examined by looking at the effect of reactor length and diameter on the rate laws and on methanol yield in
the effluent stream. The second stage assumed a non-isothermal model, using the Nussult number
correlation for heat transfer in pipes. A final optimization was completed which fully considered the
effect of changing temperature on the reverse reaction rate constant, k.
The methanol to gasoline design required extensive literature research. Attempts were made to model this
design in UNISIM by examining the reaction heats of formation for various large aromatics and longchain hydrocarbons. After these attempts were unsuccessful in accurately modeling these reactions, a
Polymath model was created to better understand the design. The design process, as well as the results, is
described below.
The process of converting synthesis gas to the product: large chain hydrocarbons takes place in two
phases, the first phase consists of a plug flow reactor operating at 2000 kPa and 400C. The reactor uses
synthesis gas (CO/CO2/H2) under high temperatures and pressures to commercially produce methanol
from synthesis gas. The catalyst used in this reactor is a Copper/Zinc-based oxide catalyst (dp = 5.5 mm,
c = 1063 kg/m3). According to the work done by Panahi et al, the methanol synthesis kinetic data
provided by Vanden Bussche and Froment (1996), adequately describes this system. Heat and mass
transfer, as well as diffusion within the catalyst pellet were incorporated into the rate constants. Catalytic
activity was assumed to be constant in this model as data was not available for more extensive process
modeling. This assumption is likely inaccurate as the catalyst pellet activity would vary as more synthesis
gas is converted to product, varying with both time and reactor length. It is also assumed that the reactor
operates in a single phase.
The equation for the rate law, based on equations (16) and (17) above, is provided below.
(16)
√
(
)
(17)
√
The constants (kj) in the above equations follow the general Arrhenius equation. In the table below, A is
the frequency factor and B is the activation energy for each reaction. These constants are presented in
Table 19.
Table 19. Methanol Synthesis Frequency Factors for Kinetic Equations.
k1
k3
A
B
A
1.07
36696
3453.38
74
√
k5
k2
B
A
B
A
B
A
B
0.499
17197
6.62*10-11
124119
1.22*1010
-94765
103066/T - 10.592
10-2073/T + 2.029
The kinetic rate laws include both forward and reverse parts, as both the methanol synthesis reaction and
the water gas shift reaction are assumed to be in equilibrium. For
and
, the assumption was made
that over the length of the reactor, the values for these two constants were constant. While this is not
indeed the case, as the reactor is not truly isothermal, this simplification allowed the team to create an
initial model for these reactions in UNISIM.
Furthermore, it is important to understand that the single pass yield in the synthesis gas to methanol
reaction is only approximately 12% methanol when optimized. While this yield is not very high, the
addition of a recycle stream, to return unreacted CO and H2 to the reactor can increase the exiting
methanol yield to approximately 41%. This is because additional reactants are being fed into the reactor.
Furthermore, by running the product methanol stream through a flash column run at 1640 kPa and 60oC,
the methanol concentration leaving the first stage of the reactor process is approximately 99.3% methanol.
Reactor models for isothermal, non-isothermal and adjusted non-isothermal models were created in order
to optimize the synthesis gas to methanol reactor with as few assumptions as possible. With each new
model, additional assumptions were eliminated to verify the quality of the data. The initial model, which
assumed an isothermal reactor, was created to test the assumption made in the paper from which the
kinetic model came. Because of the highly exothermic nature of the reactions involved, a non-isothermal
model was created to account for the heat of reactions in the model. Finally, an adjusted non-isothermal
model was created to account for changes in the rate law not accounted for in the initial and secondary
models. In the following sections, an explanation of the models is presented.
6.3.1 Modeling in an isothermal PBR
To test the assumption made in the paper which presented our kinetic model, an isothermal model was
created in UNISIM. This was accomplished by forcing the inlet and outlet temperature to the same
number in UNISIM. The results were optimized in the following way.
The reactor length was examined by plotting the reaction rates of both the water gas shift and methanol
synthesis reactors as a function of reactor length. In Figure 39, it is clear that at a reactor length of
approximately 12.5 m, the reaction rates are nearly zero, rendering the last 2.5 m of the reactor useless.
We wanted to optimize this because too small of a reactor length would not allow the reactions to reach
completion, however too long of a reactor length would be wasteful as we would not be seeing an
increase in yield by increasing the volume further. With this in mind, we eliminated extra space, making
the new reactor length 12.5 m.
75
Reaction Rate (kgmoole/m3 s)
0.010
0.005
MeOH Synthesis
0.000
0
5
10
15
RWGS
-0.005
-0.010
Reactor Length (m)
Figure 39. Reaction Rates plotted in an isothermal reactor to determine optimum length.
Using the length determined to be optimal in Figure 39, the concentration of methanol in the effluent was
plotted as a function of reactor diameter in order to determine the optimum volume of the reactor. In
Figure 40, the data is shown.
0.45
0.40
MeOH Yield
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0
2
4
6
8
10
12
Reactor Diameter (m)
Figure 40. Verification of an optimal reactor diameter for the MeOH production reactor.
Clear from the figure is that the methanol yield increases until a diameter of approximately 7 m, before
leveling off. This is important as it is this leveling off which signals an ideal reactor volume. If too low,
the volume of the reactor can hinder the production of methanol, however, if the reactor volume is too
large, it too will not be efficient, as the reactor would require more space without the return of larger
76
effluent yields of methanol. Due to the results presented in Figure 40, a reactor diameter of 7 m was used.
The result of this analysis was a calculated reactor volume of 481 m3.
The inlet temperature of the isothermal reactor was next when it came to optimization. Using a
temperature range of 250-750oC, the conversion to methanol was plotted against the inlet temperature of
the reactor. Figure 41 illustrates the results.
0.5
Maximum at 403.6 C
0.45
Conversion to MeOH
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
200
300
400
500
600
700
800
Temperature (C)
Figure 41. Optimizing Isothermal Reactor Temperature.
From Figure 41, the best scenario is the one which results in the lowest temperature, while still producing
the maximum amount of product methanol. Between the reactor temperatures of approximately 400oC
and 550oC, the methanol yield does not change considerably. Therefore, a temperature of 400oC was
determined to be the optimal temperature for the isothermal system.
A similar analysis was done for determining the optimum pressure. Because literature which analyzed
similar systems suggested the need for extremely high pressures (approximately 80 bar in the paper by
Panahi et al) and relatively average temperatures (approximately 225oC), the increased optimal
temperature should theoretically allow for lower pressures, because rate would be taken from heat instead
of pressure. Figure 42 displays the results of the pressure optimization.
77
0.45
0.4121
0.4
0.413
0.413
0.413
MeOH Yield
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
2000
4000
6000
8000
10000
Pressure (kPa)
Figure 42. Pressure Optimization in an isothermal MeOH reactor.
Panahi and Mousavi suggested a pressure of 82 bar (8200 kPa); however, in this analysis, the yield
reaches a plateau at only 2000 kPa. Because the paper by Panahi and Mousavi merely suggests that an
elevated pressure is required for the kinetics given in
Table 19, a pressure of 2000 kPa was considered appropriate. In a professional setting, the optimal
pressure required to perform this reaction would require more research and possibly bench top testing.
However, this testing is not in the scope of the project, and therefore the 2000 kPa pressure was
determined to be appropriate for the project.
6.3.2 Modeling in a Non-isothermal Packed Shell and Tube Reactor
Since the isothermal design cannot be verified due to the largely exothermic nature of the reactions
involved, a non-isothermal design was also considered. In this simulation, length, diameter, and number
of tubes were considered, as well as the temperature at the inlet of the reactor. The temperature at the
inlet of the reactor needed to be considered because of the risk for the reaction to become out of control at
high temperature, which must be avoided.
In order to test the reactor as non-isothermal, the correlation for heat transfer in a pipe given by Transport
Processes and Fluid Dynamics was entered into the computer simulation of the design. The correlation is
displayed in equation (18). It was determined that for this analysis that the flow through the pipes is
turbulent, with
, and a
between 0.7 and 16000, and an L/D > 60. With this in mind,
equation (18) is appropriate for this design.
(18)
Similarly to the isothermal reactor optimization, the length of the reactor was optimized first. By
examining the reaction rates as a function of the length of reactor, the optimal length of reactor was easily
determined. Figure 43 displays the results of this analysis.
78
8.E-03
Reaction Rate (kgmole/m3 s)
6.E-03
4.E-03
2.E-03
MeOH Synthesis
0.E+00
-2.E-03
0
5
10
15
RWGS Reaction
-4.E-03
-6.E-03
-8.E-03
Reactor Length
Figure 43. Non-isothermal MeOH Reactor Length Optimization.
Figure 43 illustrates how the reactions progress as a function of reactor length. At approximately 12.5 m,
the methanol synthesis reaction and the water gas shift reaction are no longer showing much activity,
likely because the catalyst has adsorbed the amount of reactants it can for one pass through the reactor. It
is this information that suggests that 12.5m in length is appropriate for the non-isothermal methanol
reactor.
Tube diameter and the number of tubes in the reactor system were optimized, holding the overall reactor
volume constant at 481 m3, the optimal volume from the isothermal reactor design. Because these two
components are directly related when considering constant volume and tube length, the two variables
were optimized simultaneously. The methanol yield and the outlet temperature were examined in this
analysis. The goal was to maximize the yield of methanol while minimizing the outlet temperature to
protect the catalyst (and therefore, the temperature gradient in the reactor).
The result of this analysis was that above 100 tubes, the temperature change is low and varies only
slightly with increased number of tubes. It also indicated that at 100 tubes (each with a diameter of 0.7 m)
and higher, acceptable levels of heat transfer was occurring in the reactor.
To optimize the reactor for inlet temperature, methanol yield at several inlet temperatures were measured,
recorded, and assembled in Figure 44.
79
0.15
MeOH Yield
0.14
0.13
0.12
0.11
0.1
300
350
400
450
500
Inlet Temperature (C)
Figure 44. Methanol yield in a non-isothermal reactor, varying inlet temperature
Figure 44 displays a different result than what was seen in the isothermal design. The isothermal design
showed a clear maximum in the data while the non-isothermal case did not. However, the non-isothermal
reactor shows a local peak between 400oC and 425oC, much like the isothermal reactor. Because the
isothermal optimal temperature falls within the local maximum range in the non-isothermal system, the
isothermal temperature was assumed to be optimal in the non-isothermal system as well. At higher
temperatures, the methanol yield continues to increase, but due to limitations associated with the
temperature of the catalyst, temperatures higher than 450oC were not able to be considered further.
6.3.3 Modeling in an Adjusted Non-Isothermal PBR
A third and final optimization was required where a constant reaction rate with changing temperature was
not assumed. The non-isothermal reactor was adjusted to account for these differences. This was done by
first determining the reverse equilibrium rate constant by modeling each reaction k in Microsoft Excel,
and combining these k’s to produce an exponential graph, similar to an Arrhenius equation fit.
In
Table 19, the reverse reaction constants are presented. Each rate constant was fit to an exponential curve,
and the data gathered from these graphs was entered into the UNISIM non-isothermal model.
80
1.8E+16
0.03
1.6E+16
y = 1.346e-2,912.63x
R² = 1
0.02
Reverse Reaction K
Reverse Reaction K
0.025
0.015
0.01
0.005
1.4E+16
1.2E+16
1E+16
8E+15
6E+15
4E+15
2E+15
0
0.0014
a.
y = 1.186E+10e6,658x
R² = 1
0.0016
0.0018
0.002
0
0.0014
0.0022
1/Temperature, Kelvin
b.
0.0016
0.0018
0.002
1/Temperature, Kelvin
Figure 45. Determining reverse reaction rate constants for the methanol synthesis reactor.
a. Methanol Synthesis reaction, b. Water Gas Shift Reaction
The data presented in Figure 45 is set to run as an Arrhenius equation, which fits nicely into the UNISIM
model. This data was used to re-optimize the non-isothermal model to account for the temperature change
in the reactor. When compared to the assumption where reverse reaction rate are constant, this model
provides a much more accurate look at the overall reaction rates, thereby changing many aspects of the
overall design.
The length of the reactor was the first thing to be optimized. By comparing the rates of reaction similarly
to Figure 39 and Figure 43, a reactor length of 5.4 m was determined to be the optimal size for this
system. This is different than the 12.5 m which had been determined to be the optimal length in the
previous two optimizations.
This difference could be accounted for, since the new reverse reaction rates now produced a design that
was equilibrium limited. By examining the conversion of reactants in this new design, a smaller diameter
was determined to be feasible, without sacrificing methanol yield. Additionally, when the feed was scaled
up, the recycle stream was overwhelmed by an excess of carbon dioxide, and required a larger purge
stream. This fact, coupled with the equilibrium limitations of this system, led to the smaller reactor
diameter.
Additionally, the number of tubes and corresponding tube diameters were also optimized holding the total
reactor volume constant at the new volume, 212 m3. Diameters ranging from 6 cm to 0.7 m were tested in
order to find the optimal size, considering both methanol yield and the temperature rise associated with a
given tube diameter. From theory, we determined that the smaller tube size would be beneficial, as a
81
0.0022
0.414
0.413
0.412
0.411
0.410
0.409
0.408
0.407
0.406
0.405
500
Temperature (C)
MeOH Yield
smaller tube diameter results in a greater heat transfer area, and therefore, a smaller temperature rise.
Figure 46 displays the results of this analysis.
480
460
440
420
400
0
0.1
0.2
0.3
0.4
100
1000
Diameter (m)
10000
# of Tubes
Figure 46. Determining optimal sizing constraints in an adjusted non-isothermal reactor.
In Figure 46, the outlet temperature and outlet methanol concentration are compared at varied tube
diameters. Important to note is the fact that while it appears the methanol concentration drops off
dramatically, the scale of the y-axis illustrates that the drop is actually fairly minimal. Therefore, the real
test is to determine the number of tubes that best removes excess heat while maintaining the lowest
number of tubes possible. Figure 47 displays the temperature profile at varied tube diameters.
750
Temperature (C)
700
650
600
D = 0.313 m
550
D = 0.15 m
500
D = 0.11 m
D = 0.1 m
450
400
0
5
10
15
Length of Reactor (m)
Figure 47. Temperature profiles in an adjusted non-isothermal reactor.
Combining the information provided by Figure 46 and Figure 47, the tube diameter chosen as the
optimal case was 10 cm. Holding volume of the reactor constant, this corresponds to approximately 5000
82
tubes. This provides an acceptable amount of heat exchange, allowing the temperature to remain in an
acceptable range for the chosen catalyst.
Pressure was also optimized over the range of 500 to 5000 kPa. It was optimized by considering the
reactor duty and the outlet methanol concentration at varied pressures. Figure 48 illustrates the effect of
pressure on the outlet methanol yield.
0.45
MeOH Yield--No Recycle
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
500
1000
1500
2000
2500
Pressure (kPa)
Figure 48. Effect of pressure in an adjusted non-isothermal reactor.
In Figure 48, it appears that a pressure of 1000 kPa is all that is required for this system to reach
maximum yield. At higher pressures, the increase in methanol conversion is not significant. However,
because the pressure drop across the reactor becomes excessive and the catalyst loses some of its
functionality at lower pressures, the pressure of the reactor was set to 2000 kPa.
Finally, the inlet temperature of the reactor was optimized over the range of 200oC to 425oC. The local
maximum that develops at 300oC is surprising, however at temperatures greater than 400oC the data is
expected, as shown in Figure 49. Because the gas entering the reactor from the purification system is
entering at 920oC, a higher temperature is best to avoid large cooling costs. However, at temperatures
much greater than 400oC, the temperature rise throughout the reactor is too large to protect the catalyst
from degradation.
83
0.414
0.412
MeOH Yield
0.41
0.408
0.406
0.404
0.402
0.4
200
250
300
350
400
Inlet Temperature (C)
Figure 49. Inlet temperature optimization for an adjusted non-isothermal reactor.
The information presented in Figure 49 combined with known temperature restrictions of the catalyst
involved in this reactor, led to an inlet temperature of 400oC being used for the adjusted non-isothermal
design. When the y-axis scale of Figure 49 is considered, the outlet concentration of methanol is
essentially constant.
6.3.4 Cu/ZnO-Al2O3 Catalyst
Methanol is an important feedstock in chemical industries for many value-added products with a
worldwide demand of ca. 47 Mt year (2010)83. It is industrially produced from synthesis gas mixtures
(H2/CO2/CO) at elevated pressures and temperatures over Cu/ZnO/Al2O3 catalysts. Furthermore, recent
interest in this catalytic system is due to the potential of methanol as a sustainable synthetic fuel, which
can be obtained by hydrogenation of the greenhouse gas CO2.
Due to the enormous economic relevance of the methanol synthesis reaction, the optimization of the
preparation of catalytically very active “methanol copper” is far more advanced than the fundamental
understanding of its high catalytic activity. The nature of the active site on Cu/ZnO-based highperformance catalysts for methanol synthesis has been debated in literature and is still not
comprehensively understood.
Industrial Cu/ZnO-based catalysts are prepared by a co-precipitation method developed in industry in the
1960s, which leads to formation of porous aggregates of Cu and ZnO nanoparticles.
83
Behrens, Malte , Felix Studt, Igor Kasatkin, Stefanie Kühl, and Michael Hävecker. "Methanol Synthesis over
Cu/ZnO/Al2O3: The Active Site in Industrial Catalysis."
84
One important key to high performance is a large accessible Cu surface area, which has been observed to
scale linearly with the activity for sample families with a similar preparation history84. Different
“qualities” of Cu surfaces can be prepared, which vary in the activity of their active sites and/or in the
concentration of these sites. Hence, methanol synthesis over Cu appears to be a structure sensitive
reaction.
The catalyst used in the methanol synthesis reactor is a Copper/Zinc Oxide catalyst. According to the
research done by Panahi and Mousavi, the catalyst properties were set in accordance. Using a catalyst
density of 1063 kg/m3 and a particle diameter of 5.5mm, the results were set up to run in UNISIM.
A practical methanol synthesis process requires a high performance catalyst, which must be highly active
and selective for methanol synthesis and also stable for a long period in a continuous operation. NIRE and
RlTE have been doing a joint research on methanol synthesis by catalytic hydrogenation of carbon
dioxide85. The authors have elucidated the role of metal oxides contained in Cu/ZnO-based ternary
catalysts, and then developed Cu/ZnO-based multi-component catalysts containing two or three metal
oxides.
Chinchen et al. have reported that the activity of a variety of copper-based catalysts for methanol
synthesis is proportional to the specific copper surface area irrespective of the coexisting metal oxide
(support). In other words, the methanol synthesis activities per unit copper surface area are identical.
In our design, a 50wt% Cu/45wt% ZnO/5wt% Al2O3 catalyst was used86. This was as prescribed in the
paper by Panahi and Mousavi.
6.3.5 Modeling of a Flash Column and Recycle
A flash column was designed to separate the product methanol from unreacted synthesis gas. The vessel
was optimized to run at 60ᵒC and at 12.5 atmospheres. At this temperature and pressure, the liquid stream
leaving the vessel contains 99.4mol% methanol, which the remaining 0.6mol% being a combination of
CO and CO2. This small amount of impurity has little to no effect on the reaction vessel which follows
this step, and is considered suitable to continue in the process. This liquid stream is sent directly to a heat
exchanger to be heated for the methanol to gasoline reactor.
The gas stream, containing mainly unreacted CO, CO2, and H2, and a small amount of methanol, is
returned to the reactor. However, not all the gas stream leaving the vessel is returned to the reactor. Some
of the gases are vented to the atmosphere to avoid a build-up of CO2 in the reactor, hindering the reaction.
6.3.6 Materials of Construction
The kinetics associated with the synthesis of methanol calls for extreme pressure (2000 kPa) in the
reactor. As a result, the material of construction for the reactor and surrounding units is extremely
important. The material must be strong enough to allow for these conditions, but remain economically
84
Matsuhisa, Toshio. "Structure of Active Sites of Cu-ZnO Catalysts and Selective Formation of Relevant
Precursors."Catalysis 1 (1999): 1-20. Print.
85
Saito, Masahiro, Masami Takeuchi, Tadahiro Fujitani, Jamil Toyir, Shengcheng Luo, Jingang Wu, Taiki
Wantanabe, and Yuki Kanai.
86
Panahi, Parvaneh Nakhostin, Seyed Mahdi Mousavi, Aligholi Niaei, Ali Farzi, and Dariush Salari.
85
feasible. This balance, displayed in the Table 20 below, was important in choosing a material to construct
the reactor.
Table 20. Materials of Construction Comparison for Synthesis Gas to Methanol Reactor.
Material
Pressure Strength87
Temperature Range88
Carbon Steel
Stainless Steel 304
Stainless Steel 316
Chrome/Molybdenum
3000 kPa
6270 kPa
6270 kPa
4600 kPa
-29 to 427oC
37 to 400oC
400 to 870oC
177 to 316oC
Corrosion
Resistant89
NO
YES
YES
YES, but less than
stainless steel
Additionally, the material properties regarding its ability to handle corrosive materials are extremely
important. While the high pressure is something to consider, the ability to handle corrosion takes higher
precedence because the thickness of the vessel can allow higher pressures. Because methanol tends to be
corrosive, carbon steel was quickly eliminated as an appropriate choice.
Temperature tolerance was the next factor which weighed into the decision. Due to the high operating
temperature of this unit, and using Table 20, the material used to design the reactor will be stainless steel
316. Due to its lack of corrosive properties90, coupled with the information given above, we found
stainless steel 316 to be the ideal material.
In addition, stainless steel 316 was used in the design of each piece of equipment in the synthesis gas to
methanol reactor. The reason for this is that methanol has corrosive properties that would degrade the
quality of other materials. The stainless steel lacks the susceptibility to corrosion that other materials
have, thereby making it the optimal choice for this system.
6.4 Vessel Specifications
Sizing and material specifications are listed in Appendix F. The size was determined using equations for
reactor size and suggested parameters based on process modeling in UNISIM. 316 Stainless steel was
specified for vessels that are more susceptible to corrosion with methanol, carbon dioxide and water.
Stainless steel contains more chromium than carbon steel, so it can form a passive film which prevents
rusting and flaking from spreading internally.
87
"Chromium-molybdenum alloy."PrimeChrome.
" Stainless Steel - Grade 316 - Properties, Fabrication and Applications (UNS S31600)." AZoM™ - The A to Z of
Materials and AZojomo - The "AZo Journal of Materials Online".
89
"Metals and Corrosion Resistance."Engineering ToolBox.
90
"Metals and Corrosion Resistance."Engineering ToolBox.
88
86
6.5 Control and Safety
The equipment in this unit is subject to large temperature swings. As a result, each piece of equipment
will require temperature monitoring and controllers to adjust the flows of cooling water and reactants
feeding into the system. If the system was lacking these devices, the functionality of the catalyst, quality
of the product, or the safety of the people in the facility, could be in jeopardy. Additionally, the pressure
of the synthesis gas to methanol reactor is high and will require monitoring as well.
Carbon monoxide and hydrogen gas pose safety risks to this area of the process. In addition, the product
methanol components also pose a safety risk.
Carbon monoxide (CO) is a deadly, colorless, odorless, poisonous gas. The health effects of CO depend
on the CO concentration and length of exposure, as well as each individual's health condition91. At
sustained CO concentrations above 150 to 200 ppm, disorientation, unconsciousness, and death are
possible. Hydrogen gas (H2) is a flammable, colorless, odorless gas requiring close monitoring. At high
pressures and temperatures, as is used in this facility, the risk of explosions is increased. The need for
explosion barriers surrounding these facilities is key. Finally, methanol poses serious fire hazards due to
the chemical’s high flammability. Proper fire safety precautions must be taken to ensure this facility can
operate safely.
7. Methanol to Gasoline Reactor
The Mobil process, develop by Mobil chemists/engineers in the early 1970s, is a technique for the
conversion of methanol to various gasoline components. This process utilizes a zeolite catalyst, ZSM-5,
to dehydrate and polymerize methanol into gasoline with a preference to C5+ hydrocarbon products.92
Due to unavailability of relevant process information in the literature, and recent technical advances, our
process relies on a recently studied adaptation of this process. Processes such as these are considered
potentially profitable; therefore information is underrepresented in the literature. Kinetic models are
available, but they are rather inaccurate and fail to account for specific product yields, meaning that
products are lumped into groups such as light olefins, olefins, aromatics, and hydrocarbons. While this
might suffice for preliminary estimations, these models are not specific enough for our purposes.
Fortunately, the labs of Dr. Zaidi and Dr. Pant have done extensive experimentation in this area,
specifically investigating the effects of modifying the zeolite catalyst upon the product gasoline
composition.93 From this research they have managed to produce several kinetic models, however as with
most models available in the literature, these models fail to account for specific product yields. They
have determined that when the zeolite catalyst is modified from ZSM-5 to HZSM-5 (through treatment
91
92
"CPSC - Carbon Monoxide Questions and Answers." US Consumer Products Safety Commission
"Methanol to Gasoline (MTG) Production of Clean Gasoline from Coal." ExxonMobil Research and Engineering. ExxonMobil,
n.d. Web. 7 May 2013.
93
Zaidi, H. A.; Pant, K. K. Korean J. Chem. Eng. 2010, 27, 1404-1411
87
with oxalic acid) higher total yields are observed, while maintaining this selectivity to larger
hydrocarbons. In addition to kinetic models, the original data was also included in their analysis as seen in
Table 21.
Table 21: The methanol to gasoline data, showing the relative product yields at various temperatures and
exposure times to oxalic acid treated 0.5 wt% ZnO, 7 wt% CuO, HZSM5 catalyst
Temperature (K)
635
635
635
653
653
653
673
673
673
0.129
0.09
0.047
0.129
0.09
0.047
0.129
0.09
0.047
Yield (wt%)
80.7
74.3
61.6
87.5
82.5
69.5
94.2
90.8
75.4
CH4
0.34
0.37
1.03
0.25
0.36
0.92
0.15
0.31
0.82
C2
1.00
0.92
2.10
0.72
1.04
2.38
0.52
0.89
1.82
C3
1.17
1.25
3.98
0.84
1.22
3.56
0.69
1.05
3.23
C4
0.55
0.77
2.46
0.41
0.60
2.26
0.60
0.51
2.15
C5
0.40
0.78
1.40
0.54
0.63
1.37
0.68
0.58
0.90
C5+
12.29
11.03
3.50
11.80
10.60
4.90
11.50
10.57
8.20
C6H6
0.33
0.29
0.38
1.23
1.11
0.06
0.33
0.44
0.68
C7H8
0.36
0.24
0.12
0.26
0.24
0.02
2.87
1.75
0.08
C8H10
6.73
5.08
1.40
8.86
8.21
2.72
10.47
9.30
4.32
C9H12
6.11
5.54
1.20
7.79
7.00
3.94
10.76
9.40
5.45
C10H14
0.38
0.31
0.02
1.09
0.80
0.10
0.44
0.35
0.41
CH3OCH3
2.11
2.43
5.15
1.55
2.25
5.40
1.04
1.94
3.77
Total
31.77
29.01
22.74
35.34
34.06
27.63
40.05
37.09
31.83
Water (wt% feed)
30.00
28.00
21.00
34.20
32.15
24.44
37.20
35.15
24.44
CO+CO2 (wt% feed)
18.91
17.28
17.08
17.92
16.31
17.42
16.98
18.54
19.17
Weight % MeOH remaining
19.32
25.71
39.18
12.54
17.48
30.51
5.77
9.22
24.56
W/Fao (gcat*h/gmeoh fed)
The table presents the yields of several light hydrocarbons (CH4 –C4, dimethyl ether), fuel grade
hydrocarbons and aromatics (C5-C10), and inert species (carbon dioxide and water) with respect to
various operation temperatures and catalyst exposure time. The exact kinetics are unknown at this time
due to their complexity. What is known about this reaction mechanism is that it functions similar to a
chain addition. Methanol is added to methanol, or other existing hydrocarbons, and the oxygen is
removed as water or carbon dioxide. The product of this addition is a longer hydrocarbon, eventually
88
building to aromatic compounds. The production of these larger compounds is endothermic, and is driven
by the release of energy from the formation of stable compounds such as carbon dioxide and water.
Several trends can be seen from this data: (1) The yield of larger products increases with space-time, (2)
the yield of smaller components decreases with increased space-time, (3) water production increases with
space-time and temperature and (4) the yield of larger products increases with temperature. These are all
in support of what is known about this reaction mechanism.
If this system could be modeled as isothermal, this data would be sufficient for operation. This is not an
adequate assumption as the reaction is exothermic due to the large amounts of carbon dioxide and water
formed during the process. Another isothermal option would be to utilize a CSTR set up for this system,
but this is difficult to implement due to the catalyst and gas phase reactions. The next option was to
develop pseud-kinetics from this data to model this system in polymath. This consisted of four steps: (1)
creating a rate law to determine the consumption of methanol, (2) developing individual product
correlations, (3) developing enthalpy of reaction, and heat capacity correlations, and (4) implementing the
previous three steps in a detailed polymath model.
7.1 Rate Law Determination
As the detailed kinetics of this reaction are unknown to us, and not available through the literature, it was
decided that a rate law to determine the rate of consumption of methanol was to be used. For this we
made the assumption that this could be accurately represented as a power order rate law. Using the
integral method of rate law evaluation method and Microsoft Excel first and second order reaction models
where evaluated.
0
18
y = -14.081x
R² = 0.477
-0.5
y = 116.05x
R² = 0.9119
14
12
X/(1-X)
Ln(1-X)
-1
16
y = -17.845x
R² = 0.5321
-1.5
-2
4
-3
2
-3.5
0
0.04
y = 53.224x
R² = 0.9934
8
6
y = -24.043x
R² = 0.8273
-2.5
T=635 K
10
0.09
y = 32.467x
R² = 0.9979
0.04
0.14
T=653 K
0.09
T=673 K
0.14
W/Fao
W/Fao
Figure 50: Results assuming a first order rate law (left)
Figure 51: Results of a second order rate law (right)
From these results, it is clear that the first order model does not adequately represent reality. The
relatively low R2 values at all temperatures proves that this model cannot be used. The second order
model matches the data much better at low temperatures (635 and 653 K) but is inaccurate at high
89
temperatures. If we intended to operate at temperatures below 653 K this would suffice, but for reasons
that will be discussed in the optimization section of this report this is not the case.
Next we evaluated the partial order rate laws, 1.5th and 1.75th order. These were again, evaluated with the
integral method and excel.
7
10
9
6
y = 29.619x
R² = 0.9555
4
y = 74.152x
R² = 0.9589
7
1/(1-X)^0.75
1/(1-X)^0.5
8
y = 49.063x
R² = 0.9884
5
3
2
5
T=635 K
4
T=653 K
3
T=673 K
2
y = 20.855x
R² = 0.9036
1
y = 39.321x
R² = 0.9976
6
y = 25.861x
R² = 0.9746
1
0
0
0.04
0.09
0.04
0.14
0.09
0.14
W/Fao
W/Fao
Figure 52: Results of a 1.5th order rate law (left)
Figure 53: Results of a 1.75th order rate law (right)
As the model assuming a 1.75th order reaction had a higher average R2, this was model was chosen. The
rate laws evaluated were of the form
-rate = k*(CMeOHinitial)n*(1-X)n
(19)
k=A*EXP(Ea/RT)
(20)
Where n is the order of the rate laws. Utilizing these rate laws, and the slopes of the best fit lines in
Figure 52: Results of a 1.5th order rate law (left) Figure 53: Results of a 1.75th order rate law (right), rate
constants (k) can be determined at each temperature for the n=1.75 case. The non-linear regression tool in
polymath was used to evaluate Ea/R for each case. This resulted in a low R2, meaning that while our rate
constants at each temperature are reliable, we cannot create a model for that will respond to temperature
change. This prompted the inclusion of an additional temperature modifying term:
K=A*EXP(Ea/RT)*T^m
(21)
Where m is a constant evaluated via the non-linear regression tool in polymath. This was determined to fit
our model much better, resulting in much lower residual errors. This resulted in the following rate law for
the decomposition of methanol:
-rMeOH=[10.858*EXP(-1.218E4/T)*(T2.295)]*(CMeOH1.75)*(1-X)1.75
90
(22)
Subsequent tests in polymath confirmed that this rate law accurately evaluated conversion at each tested
temperature (635, 653, and 673 K) and each catalyst exposure time within 1% of the measured
conversion.
7.2 Product Correlations
The next step towards a complete MtG model was defining correlations which would allow us to
determine the composition of the reactor effluent. Initial thoughts were to develop a model as a function
of the amount of catalyst with a temperature modifier of the form:
Xcomponent=(A*(W/Fao)2+B*(W/Fao)+C))*Tn
(23)
An equation of the form of Xcomponent=(A*(W/Fao)2+B*(W/Fao)+C))*Tn
could be integrated
down the length of the reactor in tandem with the rate law, allowing us to evaluate the product
compositions. This modeling resulted in low (<0.3) R2 values for nearly all present chemical species.
Models as a function of conversion with a temperature modifier of the form were then evaluated.
Xccomponent= (A*X2+B*X+C)*T
(24)
The residual errors were determined to decrease even further when Xcomponent was normalized to the
product yield.
X*component=Xcomponent/Xtotal
(25)
Where Xtotal is the total conversion of MeOH, Xcomponent is the weight percent of each product component
exiting the reactor, and X*component is the normalized weight percent of each product component. In several
cases, this form was determined to be inaccurate, so a second additional form was evaluated
X*ccomponent= (A*X2+B*X+C)*(T-630)n
Form B
(26)
X*ccomponent= (A*X2+B*X+C)*T
Form A
(27)
The coefficients and R2 values for each component are presented in Table 22
91
Table 22: Parameters to be used with composition correlations and their R^2 values
N
A
B
C
R2
Eq
Type
N
A
B
C
R2
Eq
Type
CH4
C2
C3
C4
C5
C6H6
3.62E-08
-6.51E-06
C5up
-0.16672
-1.94E-04
3.48E-02
1.84E-08
-3.62E-06
2.54E-08
-5.45E-06
8.11E-08
-1.55E-05
0.00018
0.895
A
0.000297
0.8419
A
0.000756
0.87454
A
5.66E-08
-1.07E05
0.00051
0.84182
A
0.000302
0.9158
A
-1.33875
0.932
B
-0.00011
0.1129
A
C7H8
C8H10
C9H12
C10H14
DME
CO
-2.45E08
4.14E-06
-0.00016
0.4874
A
7.96E-08
Water
-1.67112
4.38E-05
8.08E-08
-6.66E-08
-6.71E-08
-1.16E-05
0.000415
0.8487
A
1.47E-05
-0.00063
0.935
A
1.42E-05
-0.00059
0.9603
A
-1.61E-05
0.00083
0.9039
A
-3.94E-03
0.424845
0.83865
B
-3.74E-06
0.000688
0.88548
A
-1.82E-08
2.98E-06
-6.72E-09
1.4
1.2
1.2
1
1
wt% C10H14
wt% Benzene
Only two component models have poor correlations to the data, benzene and C10H14. When modeling
these product compositions, it is important to think of each model as a 3D surface. In the case of benzene
and C10H14 it is impossible to fit these surfaces with the number of parameters we can obtain from our
limited data sets.
0.8
0.8
0.6
T=635
0.4
T=653
0.2
0.2
T=673
0
0
0.6
0.4
60
70
80
90
100
60
Conversion
Figure 54: wt% Benzene compared to conversion (left)
70
80
90
100
Conversion
Figure 55: wt% C10H14 compared to conversion (right)
What makes these components hard to model is the fact that there compositions change drastically, and in
different ways at different temperatures as seen in Figure 54 and Figure 54. These overlaps result in needing
92
additional parameters to accurately predict the final composition for these two species. While no adequate
fit can be developed from these measurements, benzene and C10H14 correspond to approximately 1% of
the total yield. This is not a significant proportion, so while the error in predicting the amount of these
two components may be relatively large compared to the measured values, they will play a very small role
in effecting the overall yield. Upon further testing, this model error appears to be within the range of the
measurement error.
7.3 Reaction Enthalpy and Heat Capacity Correlations
The last necessary component was a model for reaction enthalpy and heat capacity. This was performed
utilizing UNISIM and the following technique.
As enthalpy of reaction data is available most commonly at 298 K, the standard scheme shown in Figure
56 was used to calculate the enthalpy of reaction. That being calculating the energy to bring the reactants
down to 298 K (ΔH1), the enthalpy of reaction at 298 K (Hrxn(25 oC, 1 atm)), and finally the energy to heat
the reactants to the reaction temperature (ΔH2). These can then be added up to determine the enthalpy of
reaction at your specified reaction temperature.
Figure 56: Theoretical model for calculating the enthalpy of reaction at any temperature
This is made more complicated by the fact that our product composition varies with temperature and
position in the reactor, and that phase changes are involved. To simplify this process, UNISIM was used
to aid these calculations. A basis stream of methanol was passed through a heat exchanger, cooling the
methanol from the desired reaction temperature to 25 oC to calculate ΔH1. Assuming the maximum
conversion at any temperature, Hrxn(25 oC, 1 atm) was calculated using enthalpies of formation and the
product composition at the reaction temperature. Finally a stream of the product composition was passed
through a second heat exchanger, raising its temperature and pressure to that of the reaction. These three
values were added, and average to obtain the average energy released per kilogram of methanol
93
consumed. This was performed for each temperature (635, 653, and 673 K), and these three points where
fit with a quadratic equation. This yielded:
Hrxn = 0.1819*T2-234.75*T+75400
(kJ/kg MeOH consumed)
(28)
The heat capacities were also modeled using UNISIM. Heat capacity of methanol was obtained from the
properties table of UNISIM, for a methanol stream at various temperatures over the range of 635-673 K.
Cp(MeOH)=0.0021*T+0.8181
kJ/(kgMeOH*C)
(29)
The product stream heat capacity was determined in a similar manner from UNISIM. A stream with the
correct product composition for that of 635, 653, and 673 K was set up and the heat capacity was obtained
on a per kilogram basis. This was then fit with a quadratic equation to give:
Cp(pdt)=0.0018*T+1.0595
kJ/(kgpdt*C)
(30)
This all relies on the assumption that the final product composition is approximately proportional to the
product composition at any point in the reactor. The effect of this assumption on heat capacity is minimal
as it has a small range over all temperatures and conversions tested. This is more of a concern for the
enthalpy of reaction correlation. If this were representative of composition at any point in the reactor, we
would expect the reaction to be more exothermic initially, due to the formation of small products, carbon
dioxide and water. The enthalpy of reaction would then decrease down the length of the reactor as more
endothermic products are formed, such as the larger aromatics and alkanes. As is, the polymath model
negates the early temperature spike, causing the temperature to be well within our set operating limits.
Unfortunately, our method of solving is not currently sophisticated enough to vary enthalpy of reaction
with temperature and composition changing over the length of the reactor. While we are limited by the
programs available to us, we do believe that this temperature cushion evident in the polymath model
should be sufficient to account for this initially more exothermic reaction. If this does prove to be the case
suggested solutions include lowering the reactor feed temperature, decreasing the initial temperature of
the coolant, increasing the flow rate of the coolant, and investigating the effects of increasing the number
of tubes (heat exchange area) present in the reactor.
7.4 Polymath Modeling
Polymath modeling: The previously derived rate law, enthalpy of reaction, and heat capacity correlations
were combined to create the polymath model presented in Appendix C: Methanol to Gasoline Kinetic
Model in Polymath. The reactor was modeled as a shell and tube heat exchange reactor, as the system has
several temperature restrictions. First, the catalyst has an upper temperature limit of 680 K, as it begins to
quickly degrade above 695 K. Second, as will be presented in the optimization section, higher
temperatures result in larger amounts of desired product being formed. Third, the reaction is exothermic,
and would result in an adiabatic temperature rise of over 100 K. This temperature rise would force us to
operate well outside our range of the model accuracy (635-673 K) regardless of the starting temperature.
Coolant Choice: The heat exchange medium was chosen to be dowtherm A, as it has a vapor phase
temperature operating range of 257 oC to 400 oC. This allows the coolant to operate at close to
94
atmospheric pressure, reducing material costs. Early simulations were performed using saturated water as
a coolant. While this minimized the early temperature spike that is common with highly exothermic
reactions, it caused the temperature of the reactor to drop well below the ideal range of operation. Using a
co-current flow of dowtherm A prevents this as the temperature of the coolant rises down the length of the
reactor. This results in a large initial temperature difference, which negates the initial temperature spike,
but a much smaller final temperature difference, which enables us to keep our final temperature within the
optimal operating range.
Pressure drop: The Ergun equation was included to account for pressure drop as our process requires the
use of catalyst. For these purposes typical catalyst densities of 1000 kg/m3, void fractions of 0.3, and
particle diameters of 8mm were assumed. The reactor was modeled using tubes of six, seven, eight cm
diameter. Analysis concluded that the optimal length for a pipe approximately 10 meters in length, having
a pressure drop of 0.2 atm over the length of the reactor. Reactors longer than this had excessive pressure
drop, and shorter than this had inadequate heat exchange area. As our design pressure is 1.25 atm
entering the reactor, it is important to minimize this pressure drop in the reactor while maintaining
adequate heat exchange.
Composition calculations: Due to the iterative nature of polymath, we had to adapt our method for solving
the product composition. Polymath produces a table of 100 step changes, evaluating the change of every
variable. Using this we evaluated the change in composition at each point with Type A:
d(Xccomponent)/dX= (A*Xm2+B*Xm+C)*Tn- (A*Xm-12+B*Xm-1+C)*Tm and Type B:
d(Xccomponent)/dX= (A*X2+B*Xm+C)*(T-630)n - (A*Xm-12+B*Xm-1+C)*(Tm-630)n
Type A: d(Xccomponent)/dX= (A*Xm2+B*Xm+C)*Tn- (A*Xm-12+B*Xm-1+C)*Tm
(31)
Type B: d(Xccomponent)/dX= (A*X2+B*Xm+C)*(T-630)n - (A*Xm-12+B*Xm-1+C)*(Tm-630)n
(32)
Where m is the point of evaluation down the length of the reactor. Using a table of conversions and
temperature produced by polymath, these derivatives were summed to compute the final composition of
the product gasoline mixture.
7.5 Polymath Optimization
Several trends were noticed in the initial data set. First, as the temperature increases, so does the yield of
desired components (those capable of being mixed with gasoline, all components present in the liquid
phase at ambient temperatures). Second, as the time of exposure to the catalyst increases so does the yield
of desired products. Before further optimization was attempted these key concepts were tested.
Temperature Effect: Table 21 illustrates that as the temperature increases, the yields of the desired
product also increase, we want to prove that this same effect is observed in a non-isothermal case. The
conditions of polymath simulations were varied to produce 3 simulations, (1) a low temperature
simulation, (2) a medium temperature simulation, and (3) a high temperature simulation as seen below in
Figure 57. These were obtained by varying the initial temperature of the reactor, and coolant as well as
the coolant flow rate, all other design variables were held constant (reactor volume, length, number of
pipes, initial pressure, methanol feed rate, and particle diameter).
95
Temperature (K)
680
670
660
High Temp
650
Medium Temp
640
Low Temp
630
0
2
4
6
8
10
Position in Reactor (m)
Figure 57: Temperature profiles of the temperature effect simulations
These temperature profiles correspond to the high, medium, and low temperatures seen below in Figure
58. As can be seen, the overall yield increases as the average temperature increases. The other surprising
trend is that the production rate of the desired products also increases with temperature. This gives us one
goal for our optimization: maintain a high average temperature to increase the production rate of the
desired components (C5, C5+, C6H6, C7H8, C8H10, C9H12, C10H14).
0.98
0.94
0.92
0.9
0.88
0.86
0.84
18000
Desired Prodcut Production (kg/h)
Total percent conversion
0.96
20000
16000
14000
Low
Temperature
12000
Medium
Temperature
10000
8000
High
Temperature
6000
4000
2000
0
Figure 58: Effect of Temperature on total conversion and production rate of desired products
Space Time effect: The exposure time increases the production rate of the desired products. To
accomplish this, the length of the reactor was manipulated while holding the number of total tubes, and
diameter constant. This ensured that the volume of the reactor and the weight of catalyst scaled with the
length of the reactor. As the reactor length increased, the total conversion increased. The reaction reaches
90% completion at less than 5m long, as can be seen in Figure 59. It then requires a doubling in length to
achieve the next 6% conversion.
96
1
Conversion of MeOH
0.98
0.96
0.94
0.92
0.9
0.88
0.86
0.84
0.82
0.8
0
5
10
15
20
Reactor Length (m)
Figure 59: Relationship between reactor length and methanol conversion
Normally this would not be considered a worthwhile investment. A doubling in reactor size to achieve an
additional 6% conversion, when 90% conversion has already been achieved is superfluous, without
additional information. Figure 60 grants us additional insight. We can see that even though the increase
in reactor length from 4.5m to 9m results in little extra conversion, it does greatly increase the yield of the
desired product. This increase in length provides us a 20% increase in the desired product production rate.
Beyond this, the reactor size effect becomes much smaller. Extra conversion gained from increasing the
reactor length from 9m to 19m is not worthwhile as a less than 10% increase in production rate require
the reactor size to be doubled. While the exact cause of this is unknown, our guess is that because this
reaction proceeds via a chain addition-esque mechanism, although methanol is not be converted at a
significant rate, the smaller components are adding together via this chain addition to create larger
hydrocarbon molecules. While this is just speculation, it is in agreement with our earlier statements about
reaction mechanism and accounts for this observed trend.
97
Desired product production rate (kg/h)
20000
19000
18000
17000
16000
15000
14000
13000
12000
11000
10000
0
5
10
15
20
Reactor Length (m)
Figure 60: Relationship between the length of the reactor and the desired product production rate
While not as definitive, this does suggest that the optimal reactor length is approximately 9m. This
corresponds with a W/Fao =0.129.
7.6 Optimization Goals
Several goals have been determined from previous testing, and several additional goals have been added
to minimize the operating cost of the reactor.
1. Maintain a high average temperature, to increase the production rate of the desired components
(C5, C5+, C6H6, C7H8, C8H10, C9H12, C10H14.
2. The reactor temperature must never exceed 680 K as the catalyst begins to degrade at higher
temperatures
3. The ideal space-time results in a W/Fao= .129 gcat*h/gmeoh fed
4. Minimize the amount of coolant to reduce cost, and the size of the compressor needed for coolant
recycle
5. Minimize the necessary heat exchange area to lower reactor cost by varying the number of tubes
in the reactor
Optimization Process: To meet these goals, the initial reactor temperature was held constant at 670 K, and
the number of tubes, coolant flow rate and coolant temperature were varied to determine the case which
allowed for the highest rate of desired product production without causing the reactor temperature to rise
above 680 K. Note that only tube diameters of 6, 7, and 8 cm were used in this analysis.
98
18200
Desired product (kg/h)
18100
18000
17900
17800
17700
Ta=550
17600
T=525
17500
17400
17300
25000
27000
29000
31000
33000
35000
Coolant flow rate (kg/h)
Figure 61: Illustrates the effect of initial coolant temperature and coolant flow rate
First the effects of initial coolant temperature and coolant flow rate were tested. The lower the coolant
initial temperature, the more effective the case is at mitigating the initial temperature spike, but this
causes the final reactor temperature to decrease. This lower final temperature decreases the yield of
desired product as previously discussed. This effect can be observed in Figure 61, as the initial
temperature of the coolant decreases, the yield of desired products decreases. The same effect is observed
as the coolant flow rate is increased, also shown in Figure 61. This is due to the final coolant temperature
decreasing as the coolant flow increases. This effect is observed because there is more dowtherm A
present to absorb the energy released by the reaction, resulting in a smaller coolant temperature increase.
This results in the reactor temperature dropping below the ideal range of operation, decreasing the yield
of desired products (C5, C5+, C6H6, C7H8, C8H10, C9H12, C10H14). Two sets of coolant temperature
are shown as initial temperatures higher than 550 K resulted in overheating of the reactor, and
temperatures lower than 525 resulted in excessive cooling. It was determined that as the coolant
temperature rises, and the coolant flow rate decreases the production rate of the desired products increase.
This is exactly what we expected to see, given the previous results that higher temperatures resulted in
higher product yields.
99
Desired Product (kg/h)
18100
18080
18060
18040
18020
18000
17980
17960
150
200
250
300
350
Number of tubes
Figure 62: Desired product formation rate with respect to number of tubes
The number of tubes was determined to have little effect upon the production rate, but did result in higher
amounts of temperature control. As this was the main effect, we opted to use the mid-range tube size, 7
cm diameter tubes. As this gives us adequate temperature control while limiting the capital cost, and
pressure drop in the tubes.
7.7 Optimization Results:
From these optimization studies, we have been able to determine the ideal case for the operation of the
reactor, which was then scaled up to accommodate a larger methanol feed stream. These optimized design
conditions can be seen below, with the product composition being shown in
Table 23. Optimized Design Conditions for the MtG reactor

Feed Temperature:
670 K (397 oC)

Coolant inlet Temperature
550 K

Coolant Exit Temperature:
679.3 K

Tmax:
679.8 K

Inlet Pressure:
1.25 atm

Outlet Pressure:
1.05 atm

Reactor Volume:
9.4 m3

Reactor Length:
10 m

Tubes:
244 (7 cm Diameter Tubes)
100

Conversion:
0.961

Heat Exchange Area:
537 m2

Total Desired Product production rate: 18000 kg/h

Coolant Flow Rate:
25,000 kg/h
To account for a larger methanol feed stream, the size of the reactor had to be increased to 26.81 m3 from
9.4 m3. To accommodate this change, only the number of tubes, heat exchange area, and coolant low
needed to be scaled-up. However, it was determined that the coolant flow rate had to be increased
significantly more than expected to achieve the desired temperature control. To maintain a maximum
temperature beneath 680 K, the coolant flow rate had to be increased approximately 5.6 times, despite the
feed, volume and number of tubes increasing by 2.8 times. This discrepancy is believed to be due to the
limited number of iterations used by polymath to solve differential equations. However, these new
conditions meet all of our previously set optimization goals: the minimum coolant flow rate is used, a
high average temperature is maintained, and the temperature never rose above 680 K. This resulted in the
scaled-up design conditions for the MtG reactor as shown below. These conditions yielded the reactor
effluent composition shown in
Table 23. Scaled-up Design Conditions for the MtG reactor

Feed Temperature:
670 K (397 oC)

Coolant inlet Temperature
600 K

Coolant Exit Temperature:
679.6 K

Tmax:
679.8 K

Inlet Pressure:
1.25 atm

Outlet Pressure:
1.05 atm

Reactor Volume:
26.81 m3

Reactor Length:
10 m

Tubes:
695 (7 cm Diameter Tubes)

Conversion:
0.961

Heat Exchange Area:
1530 m2

Total Desired Product production rate: 52000 kg/h

Coolant Flow Rate:
140,000 kg/h
101
Table 23: Product flow rates and molar compositions
kg/h
kgmol/h
Mol%
CH4
132.6
8.3
0.0019
C2
491.1
16.4
0.0037
C3
907.7
20.6
0.0047
C4
329.0
5.7
0.0013
C5
786.7
10.9
0.0025
C5up
14041.2
149.4
0.0342
C6H6
838.1
10.7
0.0025
C7H8
3997.3
43.4
0.0099
C8H10
16175.2
152.6
0.0349
C9H12
15431.4
128.6
0.0294
C10H14
798.6
6.0
0.0014
DME
1038.3
22.6
0.0052
Water
55465.5
3081.4
0.7054
CO
24025.9
546.0
0.1250
MeOH
5297.1
165.5
0.0379
Total
Desired
52068.5
4370
7.8 Methanol to Gasoline Control Loops:
A total of five control loops must be implemented us to control the methanol to gasoline reactor system.
Three of these directly affect the flow of Dowtherm A in the coolant system, and the last will impact the
flow of methanol through the reactor. All controlling effects will be implemented with PID control, and
SPC.
1. The pressure of the Dowtherm A immediately after the compressor will be measured and used to
control the energy into the compressor. This will ensure that the pressure drops throughout the
cooling system are accounted for by recompressing our coolant.
102
2. The Dowtherm A temperature will be measured as it leaves the reactor jacket. If the temperature
is too high or too low, the flow of Dowtherm A will be regulated accordingly.
3. The temperature of Dowtherm A will be measured as it leaves the steam generation heat
exchanger. This will allow us to increase/decrease the flow of saturated water or steam through
this heat exchanger to return the coolant to the temperature required by the reactor.
The final control loop impacts the flow of methanol and products through the reactor
4. The flow rate of methanol into the reactor will be measured, and used to control the flow. This
will enable us to regulate the flow through the reactor.
8. Final Separation System
The final separation of the reacted products would appear to be a complex operation due to the variety of
compounds that require separation. However, due to the differences in chemical properties, the product
stream can be broken down into three categories that are convenient to separate from one another based
on physical properties. They will be separated into light gases, organic non-polar compounds, and
aqueous polar compounds. This separation will only require further purification of the aqueous stream
and a recycle compressor for the light gases with some heat exchange.
The first step of the final separation is to cool the incoming stream with a cooler from the 400°C that it
comes in as to 20°C. At this point, the feed stream can be mixed with the stream that is getting recycled
from the vapor phase of the three-phase separator that is also around 20°C. The mixed stream flows into
a three-phase separator, which is a large refrigerated vessel that will allow the light gases to flow out the
top, the organic product phase to flow out the side, and the aqueous water phase to flow out the bottom.
The vessel must be refrigerated in order to minimize the amount of products that are lost in the vapor
phase. While optimizing the separator, we quickly became aware that the colder we made our vessel, the
less product we lost to the vapor phase and the more product we were able to capture in our fuel stream as
shown in Figure 63 and Figure 64, respectively.
103
Optimization of 3-Phase Flash Column by the
Fuel Composition in the Purge Gas Stream
0.08
Molar Flow (kgmole/hr)
0.07
0.06
0.05
0.04
y = 2E-05x2 + 0.0011x + 0.0191
R² = 0.9995
0.03
0.02
0.01
0
-25
-15
-5
5
15
Temperature (°C)
Figure 63: Amount of product fuels lost to the vapor phase at various temperatures
104
25
35
Optimization of 3-Phase Flash Column by
Product Flow Rate
67
66
Molar Flow (kgmole/hr)
65
y = -0.0018x2 - 0.1504x + 63.278
R² = 0.9978
64
63
62
61
60
59
58
57
56
-25
-15
-5
5
15
25
35
Temperature (°C)
Figure 64: Amount of product fuels produced at various temperatures for optimization
However, if the stream goes any colder than -11°C, we found that it went unstable because the aqueous
stream froze. The aqueous stream consists mostly of water with the remainder being mostly methanol
with a few other contaminants in small quantities. According to research, the methanol in the aqueous
stream causes for freezing point depression and allows us to cool our three-phase mixture even more than
the traditional 0°C, where pure water freezes94 as seen in
Table 24: Freezing point of methanol-water binary mixtures by mass percent
Freezing Point
Methanol
by
Concentration mass
(%)
by
volume
o
Temperature
F
o
C
0
10
20
30
40
50
60
70
80
90
100
0
13
24
35
46
56
66
75
83
92
100
32
0
20
-7
0
-18
-15
-26
-40
-40
-65
-54
-95
-71
-115
-82
-125
-87
-130
-90
-144
-98
According to the chart on the Engineering Toolbox website, we can cool our stream as low as -7°C, but in
order to account for a buffer of potentially more or less pure water, we decided to use a vessel
temperature of -4°C.
94
"Methanol Freeze Protected Heat-Transfer Fluids ."
105
The vapor phase, even with refrigeration, still contained large amounts of one of our desired products,
specifically hexane. Therefore, a recycle of 85% of the vapor is fed back to the feed mix point following
the first heat exchanger as mentioned previously. The reason that we chose 85% for the recycle is
because we needed to purge some of the lighter gases, but we also needed to recover a large portion of the
vapor. For control purposes, the purge ratio could be adjusted in order to accommodate varying flow
rates from the reactors by supplying more or less feed from the recycle stream. This would allow for a
consistent feed for the three-phase separator and would have a large enough of a buffer region to adjust
accordingly. This stream is heated prior to mixing with the feed to ensure that everything is in the vapor
phase and fed back to the feed using a compressor running at just under 1550hp. The remaining 15% of
the vapor stream is sent as a fuel to be burned for heat in other processes.
The organic products phase is sufficient to purity standards when exiting the three-phase separator. The
main concern for the product stream is the water composition. Traditional gasoline standards require that
no more than 10% of the fuel blend be composed of ethanol, which is the most prominent water absorber
in gasoline95. Based on the amount of water that could is inseparable from the ethanol in traditional fuel
blends based on azeotrope data, the amount of water typically in our gasoline is 0.5% 96. When
optimizing our fuel stream at various temperatures, it was clear that we were going to be well within the
industry standards as shown in Figure 65.
Mole Percent (mole water/mole)
Optimization of 3-Phase Flash Column by Water
Impurities in Product
0.0034
0.0032
0.003
0.0028
y = 6E-07x2 + 1E-05x + 0.0025
R² = 0.9867
0.0026
0.0024
0.0022
0.002
-25
-15
-5
5
15
25
Temperature (°C)
Figure 65: Water content of our product fuels exiting the separator at various temperatures
95
96
Connor, Sean. "Six things you should know about the gasoline you buy."
"Ethanol fuel." Wikipedia, the free encyclopedia.
106
35
Even at high temperatures, we are well within the range of acceptable amounts of gasoline with our fuel
blending component. Additionally, our blending component will be mixed with other fuel blending
components and traditional octane for the final blend and thus the water will be further diluted and at even
smaller percentages after mixing.
The final stream that will be exiting the three-phase separator will be the aqueous stream, which is
comprised mostly of water, but also contains methanol and other contaminants. Unfortunately, the
amount of methanol in the water is about 7% by mass, which is much too concentrated to send directly to
a waste-water treatment facility. Although we could not find any data as to how low the concentration of
methanol in water needed to be before entering waste-water treatment, it was estimated that the
concentration by mass needed to be less than 0.01% before leaving our facility. Traditional flashes were
insufficient in terms of their separation capacity due to the very small azeotrope that forms between
methanol and water. Therefore, it was necessary for our purposes to use a distillation column to separate
the contaminants from the water stream.
When optimizing the distillation column, we did not notice any difference in the separations process when
changing the number of equilibrium trays within the column. Due to the potential value of optimal
separation methods in industry, companies do not provide detailed information about their distillation
columns to the general public. We decided to ask our industrial consultant, Randy Ellenbaas, about the
sizing of our distillation column and the number of trays necessary for our process. He informed us that
the industry standard for the purposes and flow rates that we require is a distillation column of 20
equilibrium trays.
Next, we optimized the column based upon the amount of distillate product. The entering aqueous stream
contained minute amounts of light gases that were trapped within the liquid. When trying to model the
column as a total condensed liquid overhead product, the temperature requirements would be far too low
for us to achieve without expensive refrigeration tactics. Thus, we decided to use a partial condenser for
the distillate of the column so that the light gases can vent out and the column will not require
refrigeration and instead can be cooled by room temperature cooling water. The vent rate and distillate
rate directly correspond with the amount of energy that must be used to heat the reboiler at the bottom of
the distillation column, which means that the lower the rate, the less energy we will be using. In order for
the distillation column to converge, there is a minimum amount of distillate products that must be
produced to prevent tray flooding and entrainment within the column. Thus, the minimum conditions for
stability based on vent rate and distillate rate were discovered via trial-and-error in UNISIM and set as
our controlled rates. The vent rate and the distillate rate are 7kgmole/hr and 180kgmole/hr, respectively.
With these conditions, we will be using the minimum amount of energy to still achieve greater than
satisfactory purification of the waste output water. The distillation column will utilize a feedforward
cascade control system that will utilize the flow rates into the column, the temperature and pressure in the
column, the flow rates out of the column, and the purity of the water coming out of the column to adjust
the heat input and control valves accordingly.
The water stream coming out of the bottom of the column is 0.0009mass% methanol, which is much
purer than the required 0.01mass% methanol. This stream will therefore require no further separations
and will be sent to a waste-water treatment facility. The light vapor stream coming out of the partial
107
condenser will be mostly composed of carbon dioxide and methanol. This stream is sent as a fuel to be
burned for heat in other processes. The liquid coming out of the partial condenser is 88% methanol by
mass. We considered three options with the remaining methanol; further purification and sale of
industrial-grade methanol, recycling the methanol back to the methanol-to-gasoline reactor, or burning it
as heat for other processes. If we were to create an additional distillation column to further purify the
methanol to be industrial solvent grade, which is 99.8% pure, the cost would far outweigh the value due
to the market for methanol as a solvent. In order to recycle the methanol to the methanol-to-gasoline
reactor, it would need to be further purified with a small distillation column to 95% purity and the amount
that would be recycled is less than 2% of additional reactant in the reactor and would not be a significant
enough of a difference to justify building another distillation column. However, the value of the methanol
as a fuel for other processes in the plant is quite valuable as it could essentially power the entire
distillation column’s reboiler. Thus, the methanol will be mixed with the vented gas and the purge vapor
stream from the three-phase separator so that it can all be used as fuel for heating purposes throughout the
plant. For better transportation of the burning fuel stream, the combined streams will run through a heat
exchanger that will vaporize all of the compounds. The vapor fuel will be easier to transport than a half
liquid-half vapor stream. This will all be controlled using a cascade feedback control system that will
measure flow rates and temperatures and adjust valves and heating and cooling rates accordingly.
The final design and PFD for the final separations process can be seen below.
108
K-600
53
54
52
51
E-601
55
Off to Effluent
Light Gases
50
From MtG Reactor
61
E-600
V-600
Distillate
Refrigerated Cooling Fluid
59
62
63
62
E-602
56
49
48
58
Gasoline
57
Used Cooling Fluid
T-600
60
Bottoms
Figure 66: Schematic of Final Separations Process
109
9. UNISIM Model Integration
During initial design of each subunit, UNISIM cases were designed separately using the NRTL fluid
package. Once design work was nearing completion, the optimal UNISIM design for each stage was
integrated by comparing inlet and outlet streams in each model to determine the total energy consumption
and output, and the efficiency of the process.
After all the models were integrated to ensure accurate results from one subunit to the next, as many
streams as possible were integrated to provide heating and cooling inside the plant and determined the
amount of cooling water and other utilities required for this facility, and a total annual plant cost was
determined.
10. Cost Analysis
10.1 Chemical Engineering Index
When calculating costs for equipment and determining the overall costs of the plant, it is essential for all
dollar amounts to be scaled to the same year to be certain that the analysis is being performed under a
consistent basis. For this facility, Warren Seider’s “Product and Process Design Principles” was used to
determine the cost of most of the processing equipment. However, cost is subject to time, and therefore
must be scaled to account for inflation. Using equation (16), a cost index was used to scale all production
costs accordingly.
(16)
Several different cost indexes are common in industry. For the purpose of this design, we chose to use the
Chemical Engineering (CE) Plant Cost Index because it has been applied in depth in Seider97.
The costs associated with the cellulosic material to gasoline facility are scaled to the year 2012. The CE
index for the year 2012 was set at 582.498.
10.2 Equipment Cost
In determining the total capital investment of a cellulosic to gasoline facility, the cost of each piece of
equipment was determined using the Guthrie Method described in Chapter 22 of Seider. Using mainly
section 22.5, and the CE Index listed above to adjust the correlations from 2006 dollars (CE Index= 500),
which the correlations were designed for, the cost of each piece of equipment in 2012 was determined.
Costs calculated from other sources were scaled accordingly. The cost of each piece of equipment is
display in
97
98
Seider, Seader, Lewin, Widagdo, 2009
Chemical Engineering Plant Cost Index (CEPCI).
110
Table 25. The calculations performed to obtain these numbers can be found in Appendix D.
Table 25. Total Equipment Capital Cost for Cellulosic Material to Gasoline Facility.
Area Name
Feed Handling Unit
Feed Handling Unit
Feed Handling Unit
Feed Handling Unit
Component
Name
S-100
S-101
W-100
W-101
Gasification Block
Gasification Block
Gasification Block
Gasification Block
Gasification Block
Gasification Block
Gasification Block
Gasification Block
R -200
V-200
C-200
C-201
C-202
A-200
K-200
K-201
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
Syngas Purification
E-300
E-301
E-302
E-303
E-304
E-305
P-300
P-301
V-300
V-301
V-302
V-303
V-304
Claus
Propylene
Piping
Steam
Product Reactor
Product Reactor
Product Reactor
R-400
V-400
K-400
Component Type
Storage Bunkers
Feed Silo
Elevated Conveyors
Screw Conveyor
Total Feed Handling Unit Cost
Gasification Reactor
Combustion Riser
Cyclone
Cyclone
Cyclone
Air Preheater
Compressor
Compressor
Total Gasification Block Cost
Heat Exchanger
Heat Exchanger
Heat Exchanger
Heat Exchanger
Heat Exchanger
Heat Exchanger
Pump and Driver
Pump and Driver
Absorber
Sour Flash Tank
Methanol Stripper
Flash Regenerator
Hot Regenerator
Claus System
Refrigeration
Piping and Ductwork
Steam Generation
Total Syngas Purification Cost
S/T Heat Exchange Reactor
Flash Column
Compressor
111
Fixed Costs
(USD)
$6,000,000
$1,522,000
$1,975,000
$1,697,000
$ 11,195,000
$8,700,000
$12,800,000
$ unknown
$ unknown
$ unknown
$2,800,000
$660,000
$2,100,000
$ 27,000,000
$450,000
$33,500
$153,000
$119,000
$128,500
$144,000
$1,708,000
$111,000
$4,662,000
$51,000
$814,000
$786,000
$630,000
$11,411,000
$69,105,000
$1,188,000
$6,941,000
$ 91,883,000
$7,223,000
$11,901,000
$1,808,000
Product Reactor
Product Reactor
Product Reactor
E-400
E-401
E-402
Product Reactor
Product Reactor
Product Reactor
R-500
K-500
E-500
Final Separations
Final Separations
Final Separations
Final Separations
Final Separations
Final Separations
Final Separations
E-600
E-601
E-602
V-600
T-600
R-100
K-600
Heat Exchanger
Heat Exchanger
Heat Exchanger
Total Product Reactor Cost
MtG Reactor
Compressor
Heat Exchanger
Total MtG reactor Cost
Heat Exchanger
Heat Exchanger
Heat Exchanger
Three-phase Separator
Distillation Tower
Refrigerator Unit
Compressor
Total Final Separations Unit Cost
$10,853,000
$279,000
$3,944,000
$ 62,280,000
5,650,000
690,000
350,000
6,700,000
$278,000
$260,000
$296,000
$578,000
$2,935,000
$2,163,000
$1,389,000
$ 7,625,000
Table 20 better illustrates how the capital costs are divided among the sub-areas of the facility, with a
total equipment cost of about $207 million.
Table 26. Total Equipment Capital Cost by Plant Area.
Sub-Unit
Feed System
Gasification
SynGas Purification
Methanol Reactor
Gasoline Reactor
Final Separations
Total Cost
11,195,000
27,000,000
91,883,000
62,250,000
6,730,000
7,625,000
$
$
$
$
$
$
Total Equipment Cost
$ 206,683,000
10.3 Total Capital Investmentg the sub-areas of the facility, with a total equipment cost of
about $207 million.
Table 26 accounts for the total capital investment for the cellulosic material to gasoline facility, including
equipment, purchase cost, installation, direct materials and labor, freight, construction, contracting, land,
and start-up. Using the guidelines presented by Seider and Seader, as well as the economic model from
the Iowa State University biomass-to-liquids process99, the total capital investment was estimated at about
$590 million, as shown in
99
Swanson, Ryan Michael
112
Table 27. Total Capital Investment for the Cellulosic Material to Gasoline Facility.
Venture Guidance Appraisal
Biofuel Production Plant
125,728,622
Nebraska
gallons per year
2013
TOTAL
Engineered Equipment
Feed Handling
Gasification Block
Syngas Purification
Product Reactor 1
Product Reactor 2
Final Separations
Total Purchase Cost of Equipment
Total Materials Used for Installation
$
$
$
$
$
$
$
11,194,592
27,004,629
91,883,075
62,248,835
6,727,605
7,621,520
206,680,000
$
4,134,000
Total Direct Materials
Total Direct Labor
$ 210,814,000
$ 41,336,000
Total Direct Project Expenses
$ 252,150,000
Indirect Project Expenses
Freight, Insurance, Taxes
Construction Overhead
Contractor Engineering Expenses
Total Indirect Project Expenses
$
$
$
12,401,000
70,271,000
47,536,000
$ 130,208,000
Total Bare Module Cost of Engineered Equipment
Cost of Site Preparation and Service
Facilities
Alloc. Costs for Utilities and Related
Facilities
$
17,397,000
$
20,000
Direct Permanent Investment
Cost of Contingencies
$
$
$
$
$
399,775,000
$
419,764,000
$
559,964,900
$
590,382,900
25,186,000
8,395,000
33,518,000
Total Permanent Investment
Total Working Capital
382,358,000
19,989,000
Total Depreciable Capital
Cost of Land
Cost of Royalties
Cost of Start-Up
$
$30,418,000
Total Capital Investment
113
10.4 Variable Costs
Variable costs were calculated as shown in In order to calculate these costs, the cost of Miscanthus pellets
was estimated at $54.50 per short ton, based on the price and relative density of other feedstock such as
corn and switch grass. The amount of utilities, such as steam and electricity, required was calculated
based on UNISIM model outputs. The sulfur credit is based on the estimated amount of sulfur that can be
produced in a Claus unit from the amount of hydrogen sulfide leaving the Rectisol process.
Table 28. Variable Operating Costs of a Cellulosic to Gasoline Facility.
Feed Stock
Miscanthus Pellets
Methanol
Wastewater Disposal
Zeolite Catalyst
Modified Zeolite
Catalyst
Total Feed Stock:
TOTAL
207.33
0.16
0.38
4.15
¢/gallons of Gasoline Component
¢/gallons of Gasoline Component
¢/gallons of Gasoline Component
¢/gallons of Gasoline Component
0.50
¢/gallons of Gasoline Component
212.53
¢/gallons of Gasoline Component
$
267,207,000
Utilities
HP Steam
Cooling Water
Natural Gas
Electricity
Total Utilities:
Sulfur Credit
Selling/Transfer Expense
Direct Research
Allocated Research
Administrative Expense
Mgmt Incentive Compensation
Total Variable Costs:
11.82
67.38
3.72
1.81
¢/gallons of Gasoline
¢/gallons of Gasoline
¢/gallons of Gasoline
¢/gallons of Gasoline
84.74
¢/gallons of Gasoline Component
$
373,752,000
(142.02)
9.54
10.64
3.67
4.40
2.20
¢ /gallon of Gasoline Component
¢ /gallon of Gasoline Component
¢ /gallon of Gasoline Component
¢ /gallon of Gasoline Component
¢ /gallon of Gasoline Component
¢ /gallon of Gasoline Component
$
195,192,000
185.71
Component
Component
Component
Component
¢ /gallon of Gasoline Component
$233,490,000
10.5 Operating Cost
In order to calculate operating costs, 310 days of operation per year was assumed. A typical replacement
period of 3 years is assumed for the zeolite catalysts. As summarized in Table 29, utilities, labor, and
other costs were estimated as about $182 million using operating cost information presented in Chapter
23 of ‘Product and Process Design Principles.’
114
Table 29. Fixed Operating Costs of a Cellulosic to Gasoline Facility
TOTAL
Operations
Wages and Benefits
Direct Salaries and Benefits
Operating Supplies and
Services
Technical Assistance to
Manuf.
Control Laboratory
Total Operations
$
$
823,800
123,570
$
49,428
$
$
109,840
219,680
$
1,326,000
$
$
$
$
7,998,880
1,999,720
5,599,216
399,944
$
15,998,000
$
1,326,000
$
17,324,000
Maintenance
Wages and Benefits
Salaries and Benefits
Materials and Services
Maintenance Overhead
Total Maintenance
Operating Overhead
General Plant Overhead
Mechanical Department
Services
Employee Relations
Department
Business Services
$
777,164
$
262,703
$
645,812
810,002
$
Total Operating Overhead
$
2,496,000
$
19,820,000
Property Taxes and Insurance
$
39,994,000
$
59,814,000
Replacement Catalyst
$
1,951,000
$
61,765,000
Total Fixed Costs (for Cash Flow Calculations):
$61,765,000
Depreciation
Direct Plant
Allocated Plant
Total Depreciation
$
$
$
119,983,000
4,000
119,987,000
Total Fixed Costs (for ROI Calculations):
115
$ 181,752,000
$181,752,000
10.6 Return on Investment
Finally, after determining the total capital investment required to build the plant and the operating costs
associated with running the facility, the payback period was determined via the method presented in
Chapter 7 of ‘Thermal Design and Optimization’ by Adrian Bejan100. The plant has a payback period of
six years, with one year of those years devoted to construction. Cash flows of the production plant are
recorded in Appendix E. A capacity of 125.7 million gallons per year was assumed based on the amount
of product produced in the final separations stage. Also, the product selling price of $3.67/gallon of
gasoline was assumed. Based on the octane rating of the components produced in the final separations
stage (see Appendix G for calculation), the gasoline we produce could sell at the gas stations for $4.67;
one dollar was subtracted from our selling price to account for taxes and gas-station markup. An
estimated plant life of 20 years was also assumed. Based on our research of other facilities producing
gasoline, a reasonable IRR was determined to be between 10-24%.101 As summarized in Table 30, the
internal rate of return is 11.72% by the third year of production, which is a good return for a project of
this undertaking. The net present value is positive by the third year, but the return on investment is just
over one percent. The capital gains on the Dow Jones Industrial Average have been 1.6% per year from
1910-2005102, so we would expect the ROI to be at least 1.5%. However, the ROI improves over the
course of the 20 year plant life.
Table 30. Return on Investment Results
NPV
ROI (Third Year of Production)
Sales
$ 453,789,000
Variable
Costs
$(229,627,000)
Fixed Costs
$(214,506,222)
Gross
Income
Capital Investment
$
591,296,000
Income Tax
$ 9,656,000
$
(3,380,000)
Net Income
$6,276,000
$1,397,424,000
IRR
1.06%
11.72%
11. Conclusion
This report has outlined the design of a facility to convert the ornamental grass Miscanthus Giganteus first
to syngas through catalytic conversion, then methanol, and finally to gasoline. The ultimate goal of
verifying the feasibility of implementing such a process in the United States was verified. While
100
Bejan, Tastsaronis, & Moran, 1996
"Syngas: Energy from Biomass."Bioenergy. N.p., 1 June 2011.
102
Filskov, Fred
116
101
worldwide resources of fossil fuels used in the production of gasoline are dwindling, the push towards
more renewable power is becoming a dominating force, both for legislature and in engineering design.
Preliminary design, modeling, and analysis of the major units in a cellulosic to gasoline facility, including
the solid feed-handling system, a gasification block, a synthesis gas purification unit, a product reactor
block, and a final separations unit, were included as part of this report to determine an estimate of the
total capital investment that would be required to construct a facility of this size. The resulting facility is
capable of producing 125.7 million gallons (10,930 Bbl/day) of gasoline components annually, using 17
square miles of cropland. The plant could produce about 0.09% of the 134 billion gallons of gasoline
consumed in the United States. Assuming on average each vehicle uses 580 gallons annually103, our plant
would power about 216,800 cars per year.
In addition to providing capital investment estimation, we also produced a cost sheet outlining all the
associated annual costs of operation. This cost sheet includes utilities, operations, maintenance, overhead,
taxes and insurance, depreciation, and general expenses to determine a total production cost.
After performing a cash flow analysis, we calculated an internal rate of return of 11.72% with a payback
period of 6 years when our gasoline is sold at $3.67/gallon, comparable to premium gas prices.
Accounting for feedstock costs, utilities, sulfur credit, and transportation costs, variable costs total to
$1.86/gallon of gasoline while operating costs total to $1.38/gallon of gasoline. Thus, the plant is
economically feasible given today’s economic forecasts.
As illustrated by this design project, technology currently exists to produce gasoline components cleanly
from cellulosic material via synthesis gas within the guidelines of current and foreseeable regulations.
103
Wiki answers: U.S. Bureau of Transit Statistics
117
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13. Acknowledgements
Calvin College Engineering Department, for sponsoring the entire project,
Professor Wayne Wentzheimer, for serving as the team’s primary advisor for the duration of the project,
Mr. Randy Ellenbaas, the team’s consultant from industry,
Mr. Donald Honcoop, the team’s Miscanthus Giganteus consultant from Owosso, MI,
Professor Matthew Heun, for his advice on the Food Waste to Biogas project,
Ms. Ashley Waldron, Clarkson University, for information pertaining to our Food Waste to Biogas
project,
Mr. Daniel Slager, Calvin College Physical Plant, for advisement and direction regarding the Food waste
to Biogas project,
Mr. Paul Pennock, Calvin College Physical Plant, for information on the college’s cogeneration system,
Mr. Henry Kingma, Calvin College Physical Plant, for information on the college’s food waste
production,
Calvin College Engineering 2012 Senior Design Team 15, for guidance regarding our Economic Analysis
section
Mr. Nathan Gelderloos, for his assistance on the team website,
Mr. Joel Smit, for assisting on the team’s business plan,
Our Friends and Families, for all their support.
125
14. Appendices
126
Appendix A: Scheduling Details for the Cellulosics to Gasoline Project
The following list of deadlines is the work that was done throughout the project. In section A.1, all of the
tasks and overall goals of the final project are laid out and a brief estimation of the time it will take to
complete the task is also listed.
A.1 General Deadlines














WBS completion (2 hours per person) (October 5)
Scheduled WBS (October 15)
o Detailed task list, along with duration, starting and end date
o Show dependencies between tasks
Verbal Presentation (October 15) (1.5 hours of preparation)
o 5 minute presentation
o Introduce yourself, the project and scope of the project
o What will you do, why? what problem are you trying to solve
Revised WBS (October 15) (1 hour per person)
o More detailed Gantt chart
o Critically linked task list
Project Brief (October 17) (2 total hours)
o Prepare a 2-5 page summary about the project for Randy Elenbaas
o Must include pictures of each team member with names under each one
o Brief description of your project, the requirements, indicate status, and any major hurdles
Consultant Review/Website Posted (1 hour)
o Bring detailed requirements, detailed task specifications, detailed budget, and schedule.
o Bring along design documents, test reports, and any other information the reviewer might
wish to see or ask you about.
o Each team member should be ready to describe in detail their role on the team.
o Take notes during the review (or appoint one team member to do so)
PPFS Outline (October 22) (5 hours total)
o Project proposal and feasibility study, listing anticipated chapters, sections and subsections
Preliminary Cost Outline (5 hours)
o Research equipment, materials, processes, energy requirements, and catalyst costs
Draft PPFS (November 12) (3 hours)
o Exactly what it looks like
Updated Poster (November 14) (1 hour)
2nd Verbal Presentation (November 28) (2 hours of preparation)
o Between 7 and 9 minutes
o Requirements posted on Moodle
Final PPFS (December 7) (4 hours)
Updated Website (February 15) (1 hour)
Contemporary Issue Paper (February 20) (3 hours)
o 4-5 pages relating a contemporary issue to the project
127











o Individual Assignment
Project Brief (February 25) (2.5 hours)
o Prepare a 2-5 page summary about the project for Randy Elenbaas
o Must include pictures of each team member with names under each one
o Brief reintroduction of project, the design decisions, indicate status, and any hurdles
rd
3 Verbal Presentation (February 25) (1.5 hours)
o Between 6 and 7 minutes
o Requirements posted on Moodle
Industrial Consultant Review (March 4) (1 hour)
Updated Website (March 27) (1 hour)
Executive Summary for CEAC (April 1) (3 hours)
o 5-10 pages highlighting engineering design decisions
Draft Design Report (April 19) (4 hours each)
CEAC Review (1 hour)
o 25 min presentation on project
o 10 min Q&A
Final Verbal Presentation (April 22) (1 hour)
o Between 9 and 10 minutes
o Overview of Entire Project
Faculty Review (April 29) (1 hour)
Finalized Website (May 6) (1 hour)
Final Design Report (May 8) (3 hours)
A.2 Task List
Between September and May, several deadlines were put in place to ensure that the team remains on track
to finish the project by May 4, 2013. A task list was put in place, and can be viewed in the following
pages. The majority of the work for the project took place in the months between January and May. By
the end of the fall semester, the team had completed the research phase of the Cellulosic Material to
Biofuel project.

Research
o Reactors (20-30 hours)
 Can we simulate this in UNISIM
 Gasification rate laws
 Steam aided Gasification rate laws
 Tar reformer chemistry /rate laws
 Water-Gas Shift Rate laws
 Effect of sulfur and nitrogen containing components
 Effect of Feed composition on reactor conditions and product
 Product Formation
 Catalyst
 Rate laws
 Current Literature
128
o

Separations(20-30 hours)
 Tar purification
 Absorbers/reactive absorption conditions
 Purification of reactor 4 Feed
 Reactor 4 Effluent Purification
 Product purification
 Additional purification to reagent grade
 Methods of product purification
 Azeotropic/specialized distillation techniques
o Feed System(10-20 hours)
 Literature search
 Patent search
 Incorporation of an inert gas
o Output (10-20 hours)
 Determine the optimal product
 Azeotropic data
 Ease of purification
 Catalysts available for Synthesis gas
 Availability of catalysts
 Costs of catalyst
Process Design
o Reactors (50+ hours)
 Modeling reaction systems using UNISIM/Polymath (33 hours)
 Gasification (6 hours)
 Steam aided Gasfication (5 hours)
 Tar Reformer ( 6 hours)
 Water-gas Shift (6 hours)
 Product Formation (10 hours)
 Reactor Type (10 hours)
 Catalyst characteristics (5 hours)
 Optimal reaction condition (20 hours)
o Separations (50+ hours)
 Reactor Effluent Purification (5-10 hours)
 Removal/purification of Tar (10-15 hours)
 Rigorous purification of final product (10-15 hours)
 Distillation train
 Azeotropic Distillation
 Absorption
 Reactive absorption
 Modeling and optimizing separation techniques (10 hours)
 Waste Disposal
o Feed System (50+ hours)
129






Cellulosic Processing (5 hours)
 Drying
 Crushing
Delivery into reactor 1 (15 hours)
 Pressurized environment
Piping systems (15 hours)
 Materials of construction
 Size
General equipment (15 hours)
 Material construction
 Heat exchanger design
 Compressor design
 Pump design
Waste Disposal (5 hours)
Plant cost analysis (20 hours)
A.3 Project Gantt Chart
A summary of tasks for the project is shown in Table A.31. These tasks are sequenced in the Gantt charts
in Appendix B.
Table A.31. Task List
Task
Mode
1
Task Name
Duration
Start
Scheduled WBS
0 days
Mon 10/15/12 Mon 10/15/12
2
3
4
5
6
Verbal Presentation
Project Brief
Consultant Review
Website Posted
PPFS Outline
2 days
2 days
1 day
2 days
3 days
Sun 10/14/12
Tue 10/16/12
Wed 10/24/12
Tue 10/23/12
Sun 10/21/12
7
8
9
Preliminary Cost Outline
Draft PPFS
Update Poster
4 days
4 days
1 day
Tue 11/6/12
Fri 11/9/12
Wed 11/7/12 Mon 11/12/12
Wed 11/14/12 Wed 11/14/12
10
11
Verbal Presentation
Final PPS
3 days
6 days
Mon 11/26/12 Wed 11/28/12
Sat 12/1/12
Fri 12/7/12
12
Preliminary Research
Reactions
10 days
Mon 10/8/12
Fri 10/19/12
10 days
Fri 10/12/12
Thu 10/25/12
10 days
Fri 10/12/12
Thu 10/25/12
13
14
Preliminary Research
Separations
Preliminary Research
Feedstock
130
Finish
Mon 10/15/12
Wed 10/17/12
Wed 10/24/12
Wed 10/24/12
Tue 10/23/12
15
Preliminary Research
Output
10 days
Fri 10/12/12
Thu 10/25/12
16
17
Research Reactors
Research Separations
10 days
10 days
Thu 1/3/13
Sat 1/12/13
Wed 1/16/13
Thu 1/24/13
10 days
Wed 1/16/13
Tue 1/29/13
10 days
10 days
Fri 2/1/13
Thu 2/14/13
Thu 2/14/13
Wed 2/27/13
18
19
20
Model Reaction System
Gasification
Model Tar Reformer
Model Product Formation
21
Determine Reaction
Condition
10 days
Wed 2/27/13
Tue 3/12/13
22
23
Research Feed System
Research Output
7 days
7 days
Mon 1/7/13
Mon 1/14/13
Tue 1/15/13
Tue 1/22/13
24
Model Removal of Tar
7 days
Mon 3/4/13
Tue 3/12/13
Mon 2/18/13
Fri 3/1/13
Mon 3/11/13
Tue 3/19/13
25
26
27
Determine rigorous
10 days
purification of final product
Model separations
7 days
7 days
Mon 3/18/13
Tue 3/26/13
7 days
Mon 4/1/13
Tue 4/9/13
29
Analyze piping systems
Determine general
equipment design
Manage Disposal
7 days
Mon 4/8/13
Tue 4/16/13
30
31
Plant Cost Analysis
Report Finalizing
7 days
7 days
Mon 4/15/13
Tue 4/23/13
Tue 4/23/13
Wed 5/1/13
28
131
Appendix B: Gantt Chart for Each Semester
Figure B.1. Gantt chart for first semester, corresponding to the task list shown above
132
Figure B.2. Gantt Chart for second semester, corresponding to the task list shown above
133
Appendix C: Methanol to Gasoline Kinetic Model in Polymath
Variable Initial value Minimal value Maximal value Final value
1
a
1530.169
1530.169
1530.169
1530.169
2
alpha
0.0108268
0.0108268
0.0108268
0.0108268
3
beta
1284.108
1284.108
1284.108
1284.108
4
CA
2.684123
2.684123
2.684123
2.684123
5
Cmo
22.73616
22.73616
22.73616
22.73616
6
Cpc
1.849007
1.849007
2.081388
2.081388
7
Cpm
2.2251
2.2251
2.245564
2.245564
8
Cpp
2.2655
2.2655
2.28304
2.28304
9
D
0.0700838
0.0700838
0.0700838
0.0700838
10 density
1000.
1000.
1000.
1000.
11 Dp
0.008
0.008
0.008
0.008
12 Fmo
1.454E+05
1.454E+05
1.454E+05
1.454E+05
13 G
15.04733
15.04733
15.04733
15.04733
14 Hrxn
-227.59
-227.59
-122.6538
-122.6538
15 k
296.2135
296.2135
397.3555
397.3555
16 L
10.
10.
10.
10.
17 Lreactor 0
0
10.
10.
18 mc
1.4E+05
1.4E+05
1.4E+05
1.4E+05
19 Pdt
0
0
1.398E+05
1.398E+05
20 Po
1.25
1.25
1.25
1.25
21 R
8.206E-05
8.206E-05
8.206E-05
8.206E-05
22 rm
7.012E+04
229.7158
7.012E+04
229.7158
23 T
670.
670.
679.7447
679.7447
24 Ta
600.
600.
679.6498
679.6498
25 To
670.
670.
670.
670.
26 tubes
695.
695.
695.
695.
27 U
100.
100.
100.
100.
28 V
0
0
26.81
26.81
29 void
0.3
0.3
0.3
0.3
30 Vol
26.81
26.81
26.81
26.81
31 X
0
0
0.9611806
0.9611806
32 y
1.
0.8404495
1.
0.8404495
134
Differential equations
1 d(X)/d(V) = rm/Fmo
MeOH conversion reaction, controlled by rate constant derived from physical data
2 d(T)/d(V) = (U*a*(Ta-T)-rm*Hrxn)/(Fmo*(Cpm+(Cpp-Cpm)*X))
3 d(Ta)/d(V) = U*a*(T-Ta)/(mc*Cpc)
Dowtherm A temperature down the length of the reactor
4 d(y)/d(V) = -alpha/(2*y)*(T/To)
P/Po
Explicit equations
1
Fmo = 145400
Feed mass flow of methanol (kgMeOH/h)
2
U = 100
kJ/(h*m^2*K) for liquid water
3
tubes = 695
m (187 - D=8cm, 244 - D=7cm, 332 - D=6cm)
4
density = 1000
kg/m^3 catalyst
5
Po = 1.25
atm, used to calculate initial methanol feed concentration
6
To = 670
7
R = 8.205746E-5
m^3atm/(k*mol)
8
L = 10
9
Hrxn = 0.1819*T^2-234.75*T+75400
Correlation derived from "gibbs-esque" reactor (kJ/kg MeOH consumed)
10 Cpm = 0.0021*T+0.8181
Correlation derived from "gibbs-esque" reactor (kJ/kgMeOH C)
11 Cpp = 0.0018*T+1.0595
Correlation derived from "gibbs-esque" reactor (kJ/kgpdt C)
12 Vol = 26.81
13 Cpc = -2.4E-6*Ta^2+0.0059887*Ta-0.880213
Dowtherm A heat capacity for vapor phase (300-400 C)
14 Pdt = (Fmo*X)
15 void = 0.3
16 k = 10.858*exp(-1.218E4/T)*T^2.295*(1-void)*density
Rate constant derived from experimental data (mol*kgMeOH/(m^3*h*kgcat)), 700 converts from catalyst to volume
17 D = sqrt(4*Vol/tubes/(3.1415*L))
Calculated the diameter fo teh tubes to be used
18 a = 3.1415*D*L*tubes
135
m^2
19 Lreactor = 4*V/tubes/3.1415/D^2
Used to plot variables down the length of the reactor
20 Dp = 0.008
21 CA = 3.145159*D^2/4*tubes
22 Cmo = Po/(R*To)
Simple model for MeOH gaseous concentration
23 G = Fmo/3600/CA
24 rm = k*(Cmo*(1-X)*(To/T)*(y))^1.75
Rate equation for the depletion of MeOH
25 beta = G*(1-void)*1.75*G/(density*Dp*void^3)
26 mc = 140000
coolant flow rate (kg/h)
27 alpha = 2*beta/(CA*(1-void)*(density))/(Po*101)
136
Appendix D: Equipment Cost Calculation Sheets
The following calculation sheets show the equations used to determine the cost of the Miscanthus to gasoline plant equipment. Unless otherwise
specified, all equations were taken from Warren Seider’s ‘Product and Process Design Principles.’
INPUT FORM
PROCESS:
PRODUCT:
Gasoline Additive
CAPACITY:
125,728,622
THIS YEAR:
DAYS OF PRODUCT
INVENTORY:
PER YEAR
2013
0
Biofuel Production Plant
OPERATING HOURS PER YEAR
PER HOUR UNITS:
19,201
MARKET PRICE OF PRODUCT ($):
LOCATION:
7440
gallons
3.67
Nebraska
INPUT FOR VENTURE GUIDANCE APPRAISAL (check here to include verification calculation
PURCHASED COST OF EQUIPMENT (Engineered)
Equipment Type 1
Feed Handling
Cost in $
11,194,592
Equipment Type 2
Gasification Block
Cost in $
27,004,629
Equipment Type 3
Syngas Purification
Cost in $
91,883,075
Equipment Type 4
Product Reactor 1
Cost in $
62,248,835
Equipment Type 5
Product Reactor 2
Cost in $
6,727,605
Equipment Type 6
Final Separations
Cost in $
7,621,520
Percentage of Purchased Costs for Installation Materials:
Percentage of Purchased Costs for Labor :
2.00
20.00
137
%
%
)
Indirect Project Expenses
Percentage of Purchased Costs for Freight, Insurance and Taxes :
Percentage of Purchased Costs for Construction Overhead:
Percentage of Purchased Costs for Contractor Engineering Expenses:
6.00
34.00
23.00
%
%
%
.
Total Capital Investment (see Table 9.2)
Percentage of Total Bare Module Costs for Site Preparation and Service Facilities:
4.55
%
Allocated Utility and Related Facility Costs (see Table 9.4):
19,610.00
$
Percentage of Direct Permanent Investment for Contingencies:
Land: Enter either a dollar value or a percentage of Total Depreciable
Capital:
5.00
%
Percentage of Total Depreciable Capital for Royalties:
2.00
%
Percentage of Total Depreciable Capital for Start-Up:
8.00
%
Site Factor (see Table 9.5)
1.15
$
Working Capital
INPUT FOR VARIABLE COST CALCULATIONS (see Table 10.1)
Reactants (check box on left if you intend to keep inventory of Reactant)
Reactant 1
Miscanthus Pellets
Reactant 2
Reactant 3
Methanol
Wastewater
Disposal
Reactant 4
Zeolite Catalyst
Reactant 5
Modified Zeolite Catalyst
Uni
ts
Uni
ts
Uni
ts
Uni
ts
Uni
ts
138
short ton
gallon
100 cubic
feet
lb
lb
or
6
% of
TDC
Cost ($) per short ton Miscanthus Pellets
short ton Miscanthus Pellets per gallons Gasoline
Additive
Cost ($) per gallon Methanol
54.5
0.0380422
53
2.81
0.0005736
48
3.3
0.0011551
41
15
0.0027696
68
gallon Methanol per gallons Gasoline Additive
Cost ($) per 100 cubic feet Wastewater Disposal
100 cubic feet Wastewater Disposal per gallons
Gasoline Additive
Cost ($) per lb Zeolite Catalyst
lb Zeolite Catalyst per gallons Gasoline Additive
Cost ($) per lb Modified Zeolite
Catalyst
lb Modified Zeolite Catalyst per gallons Gasoline
Additive
15
0.0003331
62
Utilities(enter the units then check the
box)
HP Steam
Cooling
Water
ton
Cost($) per : 9.55
of HP Steam per of
ton
Cost($) per : 0.36
of Cooling Water per of
Natural Gas
cubic feet
Cost($) per : 7.45
of Natural Gas per of
Electricity
kWh
Selling/Transfer Expense
Direct Research
Allocated Research
Administrative Expense
Management Incentive
Compensation:
Cost($) per kW:
2.6
2.9
1
1.2
0.063
% of Sales
% of Sales
% of Sales
% of Sales
0.6
% of Sales
kW of Electricity per of
Byproduc
139
0.01237
706
1.87189
682
0.00499
242
0.28794
589
ts
If there are valueable byproducts, click here!
Total Byproduct Credit=
INPUT FOR FIXED COST CALCULATIONS (see Table 10.1)
Operations
Number of Operators per Shift:
Annual Wages per Operator:
Direct Salaries and Benefits
Operating Supplies and
Services:
Technical Assistance to
Manuf.:
Control Laboratory:
3
(assuming 5 Shifts)
54,920
$ Includes Benefits:
% of
wages
% of
wages
in $/labor
yr
in $/labor
yr
15
6
109,840
219,680
Maintenance
Wages:
2
Salaries and Benefits:
Materials and Services:
Maintenance Overhead:
25
70
5
% of Total Depreciable Capital
Includes Benefits:
% of Maintenance Wages and Benefits
% of Maintenance Wages and Benefits
% of Maintenance Wages and Benefits
Operating Overhead
General Plant Overhead:
Mechanical Department
Services:
Employee Relations
Department:
7.1
Business Services:
7.4
2.4
5.9
% of Maintenance and Operations Salaries, Wages and
Benefits
% of Maintenance and Operations Salaries, Wages and
Benefits
% of Maintenance and Operations Salaries, Wages and
Benefits
% of Maintenance and Operations Salaries, Wages and
Benefits
140
142.02
¢ /gallo of Gasoline
Additive
Property Taxes and Insurance
Property Taxes and Insurance
10
% of Total Depreciable Capital
30
20
% of Total Depreciable Capital
% of Allocated Costs
Depreciation
Direct Plant
Allocated Plant
Catalyst Replacement
Catalyst Replacement
1950572.5
$
INPUT FOR CASH FLOW ANALYSIS
Please click the enter button on the right after entering the data in the next two lines!
Design Phase
Construction Phase
0
1
in yrs.
in yrs.
By default, the Total Permanent Investment Costs are spread evenly over the design & construction phase.
(Working Capital is always introduced in the last year of construction.) If you would like to change the default
Estimated Life
Cost of Capital
Inflation Rate
Income Tax Rate
20
1.5
3
35
in yrs.
%
%
%
Depreciation Schedule
Pick MARCS Tax-Basis Depreciation Schedule
5 year
7 year
10 year
15 year
141
Appendix E: Cash Flows*
Year
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
% of Capacity
0
45
67.5
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
$
212
$
214
$
330
$
454
$
467
$
481
$
496
$
511
$
526
$
542
$
558
$
575
$
592
$
610
$
628
$
647
$
666
$
686
$
707
$
728
$
750
$
580
$
589
$
598
Depreciation
$
(560)
$
(31)
$
(105)
$
(62)
$
-
$
(158)
$
(1)
$
(108)
$
(67)
$
(310)
$
(160)
$
(1)
$
(167)
$
(69)
$
(137)
$
(162)
$
(1)
$
(230)
$
(71)
$
(98)
$
$
(1)
$
(237)
$
(73)
$
(70)
$
$
(1)
$
(244)
$
(75)
$
(50)
$
$
(1)
$
(251)
$
(77)
$
(50)
$
$
(1)
$
(258)
$
(80)
$
(50)
$
$
(1)
$
(266)
$
(82)
$
(25)
$
$
(1)
$
(274)
$
(84)
$
(57)
$
$
(1)
$
(282)
$
(87)
$
(57)
$
$
(1)
$
(291)
$
(89)
$
(28)
$
$
(1)
$
(300)
$
(92)
$
-
$
$
(1)
$
(309)
$
(95)
$
-
$
$
(1)
$
(318)
$
(98)
$
-
$
$
(1)
$
(327)
$
(101)
$
-
$
$
(1)
$
(337)
$
(104)
$
-
$
$
(1)
$
(347)
$
(107)
$
-
$
$
(1)
$
(358)
$
(110)
$
-
$
$
(1)
$
(368)
$
(113)
$
-
$
$
57
$
(380)
$
(117)
$
-
$
$
(0)
$
(288)
$
(202)
$
-
$
$
(0)
$
(293)
$
(205)
$
-
$
$
43
$
(297)
$
(208)
$
-
Taxable
Income
$
(546)
$
(429)
$
(203)
$
(108)
$
87
$
112
$
117
$
122
$
152
$
126
$
131
$
165
$
199
$
205
$
212
$
218
$
225
$
231
$
238
$
245
$
310
$
90
$
91
$
136
$
-
$
-
$
-
$
(1)
$
(31)
$
(39)
$
(41)
$
(43)
$
(53)
$
(44)
$
(46)
$
(58)
$
(70)
$
(72)
$
(74)
$
(76)
$
(79)
$
(81)
$
(83)
$
(86)
$
(109)
$
(35)
$
(35)
$
(53)
Net
Earnings
$
(546)
$
(429)
$
(203)
$
(109)
$
57
$
73
$
76
$
79
$
99
$
82
$
85
$
107
$
130
$
134
$
138
$
142
$
146
$
150
$
155
$
160
$
202
$
55
$
56
$
83
Annual
Cash
$
(546)
$
(120)
$
(66)
$
(11)
$
127
$
123
$
126
$
129
$
124
$
138
$
142
$
136
$
130
$
134
$
138
$
142
$
146
$
150
$
155
$
160
$
202
$
55
$
56
$
83
$
(546)
$
(664)
$
(728)
$
(739)
$
(619)
$
(505)
$
(390)
$
(274)
$
(164)
$
(43)
$
80
$
195
$
303
$
413
$
525
$
638
$
753
$
870
$
988
$
1,109
$
1,258
$
(377)
$
(374)
$
(371)
Sales
Capital
Cost
Working
Capital
Variable
Cost
Fixed Cost
Income Tax
Cumulative
PV @ 1.5%
*Cash flows in millions of dollars
142
Appendix F: Equipment Specification Sheets
143
Feed Handling Specification Sheets
Storage Bunker
Identification:
Item
Storage Bunkers
Date: 5-10-2013
Name:
Item
SNumber:
100
Function: To store and maintain up to two months of feedstock storage
Operation: It will house the feedstock solution and keep it dry
Materials
Pelletized Feedstock
Handled:
Inlet
Outlet
Stream ID:
1
2
Temperature
©:
Pressure (kPa):
30
100
Design Data:
Material:
Capacity:
Concrete
176195 m^3
Comments: S-100 is a storage facility for up to two months of pelletized feedstock.
Done By:
Checked
By:
144
Philip
Reinken
Kaylea
Brase
Feed Handling Specification Sheets
Storage Bunker
Identification:
Item
Storage Bunkers
Date: 5-10-2013
Name:
Item
SNumber:
101
Function: To provide a consistent feed to the reactor feed system
Operation: It adjust the flow rate according to control feedback from the reactor
Materials
Pelletized Feedstock
Handled:
Inlet
Outlet
Stream ID:
3
4
Temperature
(C):
Pressure (kPa):
30
100
Design Data:
Material:
Volume:
Concrete
9805 m^3
Height:
12.192 m
Diameter:
9.754 m
Comments:
Done By:
Checked
By:
145
Philip
Reinken
Kaylea B
Feed Handling Specification Sheets
Elevated Conveyor
Identification:
Item
Elevated Conveyors
Date: 5-10-2013
Name:
Item
WNumber:
100
Function: To transport the feedstock from storage to feed
Operation: It will mechanically lift the pelletized feedstock to a feed silo
Materials
Pelletized Feedstock
Handled:
Inlet
Outlet
Stream ID:
2
3
Temperature
(C):
Pressure (kPa):
30
100
Design Data:
Material:
Width:
Carbon Steel
5.335 m
Length:
76.2 m
Comments: S-100 is a storage facility for up to two months of pelletized feedstock.
Done By:
Checked
By:
146
Philip
Reinken
Kaylea
Brase
Feed Handling Specification Sheets
Auger Screw Conveyor
Identification:
Item
Auger Screw
Date: 5-10-2013
Name:
Item
WNumber:
101
Function: To transport the feedstock from storage to feed
Operation: It will mechanically push the feedstock into the first reactor
Materials
Pelletized Feedstock
Handled:
Inlet
Outlet
Stream ID:
4
5
Temperature
(C):
Pressure (kPa):
30
110
Design Data:
Material:
316 Stainless Steel
Diameter:
Length:
6.8 m
20 m
Comments:
Done By:
Checked
By:
147
Philip
Reinken
Gasification Unit Equipment Specification Sheets
Gasification Reactor
Identification:
Item
Name:
Item
Number:
Gasification Reactor
Date:
5/9/2013
R-200
Function:
Operation:
Materials
Handled:
Stream ID:
Quantity
(kg/hr):
Composition:
Temperature
(C):
Pressure
(kPa):
Design Data:
Biomass
Methane
Hydrogen
CO
CO2
Ethylene
Ethane
Benzene
Toluene
Phenol
Napthalene
Water
Char (kg/h)
Outlet
700000
610000
0.873
0.0
0.096
0.022
0.434
0.168
0.027
0.000
0.011
0.000
0.000
0.003
0.249
89100
0.127
980
175
Type:
Volume:
Reactor
1325 m3
Height:
30.8 m
Diameter:
Materials of Construction:
Comments:
Inlet
Done by: Mitchell
Groenenboom
Checked By: Phil Reinken
7.4 m
Stainless
Steel
This reactor functions to convert biomass into synthesis gas, using syngas as
a fluidizing agent and hot sand as a heat source.
148
Gasification Unit Equipment Specification Sheets
Combustion Riser
Identification:
Function:
Operation:
Materials
Handled:
Stream ID:
Quantity
(kg/hr):
Composition:
Temperature
(C):
Pressure
(kPa):
Design Data:
Item
Combustion Riser
Date:
Name:
Item
R-201
Number:
The combustion of char produced in the gasification reactor
Continuous
Sand
Char
Combustion
Products
Outlet
900,000
900,000
0.937
0.063
0.000
0.937
0.000
0.063
980
185
Type:
Volume:
Reactor
750 m3
Height:
28.1 m
Diameter:
Materials of Construction:
Comments:
Inlet
5/9/2013
Done by: Mitchell
Groenenboom
Checked by: Phil Reinken
5.8 m
Stainless
Steel
This reactor combusts and disposes of char as flue gas, heating sand which is
used as an energy source for R-200.
149
Gasification Unit Equipment Specification Sheets
Compressor
Identification:
Function:
Operation:
Materials
Handled:
Stream ID:
Quantity
(kg/hr):
Composition:
Item
Air Compressor
Date:
Name:
Item Number:
K-200
Compresses air to the pressure of the combustion riser
Continuous
Oxygen
Nitrogen
T In (C):
25.0
T Out (C):
Pressure
In(kPa):
Pressure
Out(kPa):
Design Data:
93.3
101
Comments:
Inlet
Outlet
981000
981000
0.22
0.78
0.22
0.78
185
Type:
Centrifugal Compressor
Materials of Construction:
Volumetric
Flow:
Pressure Change:
Stainless Steel
850000 m3/hr
Power Required:
500 hp
Done by: Mitchell Groenenboom
Checked by: Phil Reinken
150
kPa
5/9/2013
Gasification Unit Equipment Specification Sheets
Compressor
Identification:
Function:
Operation:
Materials
Handled:
Item
Syngas Compressor
Date:
Name:
Item Number:
K-201
Compresses Air used in the combustion riser to 1.85 atm
Continuous
Stream ID:
Quantity
(kg/hr):
Composition:
Methane
Hydrogen
Carbon
Inlet
Outlet
99000
99000
0.096
0.022
0.434
0.096
0.022
0.434
0.168
0.168
0.027
0.000
0.011
0.000
0.000
0.003
0.249
0.027
0.000
0.011
0.000
0.000
0.003
0.249
Monoxide
T In (C):
T Out (C):
Pressure
In(kPa):
Pressure
Out(kPa):
Design Data:
Comments:
Carbon
Dioxide
Ethylene
Ethane
Benzene
Toluene
Phenol
Napthalene
Water
980
1028
150
175
Type:
Centrifugal Compressor
Materials of Construction:
Stainless Steel
Volumetric
Flow:
Pressure Change:
350000 m3/s
Power Required:
3300 hp
25 kPa
Done by: Mitchell Groenenboom
Checked by: Phil Reinken
151
5/9/2013
Synthesis Gas Purification Unit Equipment Specification Sheets
Heat Exchanger
Identification:
Function:
Operation:
Materials
Handled:
Item
Feed Gas Exchanger
Date: 5/10/2013
Name:
Item
E-300
Number:
To exchange energy between hot and cold streams
Continuous
Stream ID:
Quantity
(kg/hr):
Description:
THOT In (C):
THOT Out (C):
980.0
76.67
TCOLD In (C):
TCOLD Out (C):
-50.28
919.2
Pressure
(kPa):
Design Data:
2275
Cold in
11
5.929E+5
Hot In
8
6.247E+5
Hot Out
9
6.247E+5
Cold Out
17
5.929E+5
Clean
Syngas
Dirty
Syngas
Gas to
Absorber
Exit Gas
Type:
Materials of Construction:
Shell and Tube- U-tube
Carbon Steel
Heat Duty:
Heat Transfer Coefficient
9.587E+8 KJ/h
2.265E+4 KJ/h-m^2-C
Heat Transfer Area:
605.5
m^2
Comments:
Done By:
Checked
By:
152
Kaylea B.
Aimee D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Heat Exchanger
Identification
:
Function:
Operation:
Item
Methanol Bottoms Cooler
Date: 5-10-2013
Name:
Item
E-301
Number:
To exchange energy between hot and cold streams
Continuou
s
Materials
Handled:
Cold in
Propyle
ne
Stream ID:
Quantity
(kg/hr):
Description:
THOT In (C):
THOT Out (C):
Refriger
ant
Hot In
14
Hot Out
15
2.563E+
6
Column
Bottoms
2.563E+
6
Cold Out
Propylen
e
-22.62
-51.11
TCOLD In (C):
TCOLD Out (C):
Pressure
(kPa):
Design Data:
344.7
Type:
Shell and Tube
Materials of Construction:
Heat Duty:
Carbon Steel
2.515E+
8
208.1
m^2
Heat Transfer Area:
Comments:
Done
By:
Checked
By:
153
Kaylea B.
Aimee D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Heat Exchanger
Identification
:
Function:
Operation:
Item
Treated Methanol
Date: 5-10-2013
Name:
Exchanger
Item
E-302
Number:
To exchange energy between hot and cold streams
Continuou
s
Materials
Handled:
Cold
in
16
2.556E
+6
Absor
ber
Botto
ms
Stream ID:
Quantity
(kg/hr):
THOT In (C):
THOT Out (C):
96.13
-22.62
TCOLD In (C):
-36.22
TCOLD Out (C):
81.67
Pressure
(kPa):
Design Data:
3585
Hot In
Hot Out
Cold Out
24
2.563E+6
15
2.563E+
6
19
2.556E+
6
Treated
Methano
l
Type:
Floating Head Shell and Tube
Materials of Construction:
Heat Duty:
Heat Transfer Coefficient
Carbon Steel
1.105E+9 kJ/h
1.492E+5 kJ/h-m^2-C
Heat Transfer Area:
527.7
m^2
Comments:
154
Done By:
Kaylea B.
Checked
By:
Aimee D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Heat Exchanger
Identification
:
Function:
Operation:
Item
Hot Regenerator Condenser
Name:
Item
E-303
Number:
To exchange energy between hot and cold streams
Continuou
s
Date: 5-10-2013
Materials
Handled:
Cold
in
Water
7.200
E+6
Stream ID:
Quantity
(kg/hr):
Description:
THOT In (C):
THOT Out (C):
Hot In
Hot Out
Cold Out
27
4528
28
4528
Water
7.200E+
6
Regenerato
r Overhead
16.52
-12.22
TCOLD In (C):
TCOLD Out (C):
Pressure
(kPa):
Design Data:
206.8
Type:
Materials of Construction:
Fixed Head Shell and Tube
316 Stainless
Heat Duty:
3.022E+5 kJ/h
Heat Transfer Area:
4.645 m^2
Comments:
Done
By:
Checke
d By:
155
Kaylea
B.
Aimee
D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Heat Exchanger
Identification:
Function:
Operation:
Materials
Handled:
Item
Sour Flash Feed Heater
Date: 5-10-2013
Name:
Item
E-304
Number:
To exchange energy between hot and cold streams
Continuous
Cold in
19
2.556E+6
Stream ID:
Quantity
(kg/hr):
Description:
Hot In
Steam
1.570E+5
Hot Out
Steam
1.570E+5
Cold Out
20
2.556E+6
Done By:
Checked
By:
Kaylea B.
Aimee D.
Sour
Flash
Feed
THOT In (C):
THOT Out (C):
150
TCOLD In (C):
81.67
TCOLD Out (C):
98.89
Pressure
(kPa):
Design Data:
379.2
Type:
Shell and Tube
Materials of Construction:
Carbon Steel
Heat Duty:
4.307E+8 kJ/h
Heat Transfer Area:
18.58
m^2
Comments:
156
Synthesis Gas Purification Unit Equipment Specification Sheets
Heat Exchanger
Identificatio
n:
Function:
Operation:
Item
Hot Regenerator Reboiler
Name:
Item
E-305
Number:
To exchange energy between hot and cold streams
Continuou
s
Date: 5-10-2013
Materials
Handled:
Cold in
31P
6.442E+4
Stream ID:
Quantity
(kg/hr):
Description:
Hot In
Steam
3.283E+
4
Hot Out
Steam
3.283E+
4
Cold Out
32
6.442E+
4
Regenerato
r
Bottoms
THOT In (C):
THOT Out (C):
150
TCOLD In (C):
TCOLD Out (C):
16.58
148.9
Pressure
(kPa):
Design Data:
206.8
Type:
Materials of Construction:
Shell and Tube
Carbon Steel
Heat Duty:
9.011E+7 kJ/h
Heat Transfer Area:
729.3
m^2
Comments:
Done By:
Checked By:
157
Kaylea B.
Aimee D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Vessel
Identification
:
Function:
Operation:
Item
Rectisol Absorber
Name:
Item
V-300
Number:
To separate H2S from syngas
Continuou
s
Date: 5-10-2013
Materials
Handled:
Stream ID:
Quantity
(kg/hr):
Stream ID:
Quantity
(kg/hr):
Temperature
(C):
Pressure
(kPa):
Design Data:
Gas Feed
Solvent
9
6.247E+
5
10
29.07
Recycle
Side
Draw
18
5.841E+
4
13
2.582E+
6
Overhea
d
11
5.929E+5
Bottoms
Done By:
Kaylea B.
Checked
By:
Aimee D.
16
2.556E+
6
-51.1
3585
Type:
20 Tray Column
Height:
12.2 m
Diameter:
Materials of Construction:
2.1 m
316 Stainless
Comments:
158
Synthesis Gas Purification Unit Equipment Specification Sheets
Vessel
Identification:
Function:
Operation:
Materials
Handled:
Item
Pre-wash Sour Flash
Name:
Item
V-301
Number:
To regenerate methanol
Continuous
Feed
20
2.556E+6
Stream ID:
Quantity
(kg/hr):
Temperature
(C):
Pressure
(kPa):
Design Data:
Overhead
21
3.200E+5
Date: 5-10-2013
Bottoms
22
2.236E+6
98.9
379.2
Type:
Tank
Liquid Level Height:
0.6 m
Height:
Diameter:
Materials of Construction:
1.5 m
0.46 m
Carbon Steel
Comments:
Done By:
Checked
By:
159
Kaylea
B.
Aimee
D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Vessel
Identification
:
Function:
Operation:
Item
Methanol Stripper
Name:
Item
V-302
Number:
To recover methanol by removing H2S
Continuou
s
Date: 5-10-2013
Materials
Handled:
Stream ID:
Quantity
(kg/hr):
Temperature
(C):
Pressure
(kPa):
Design Data:
Feed
Recycle
21
3.200E+
5
34
5.797E+
4
Overhea
d
35
5.100E+4
Bottoms
23
3.269E+
5
76.7
379.2
Type:
Packing:
Packed Column
1 in Ceramic Raschig rings
Height:
7.6 m
Diameter:
Materials of Construction:
1.1 m
Carbon Steel Sheel
Comments:
160
Done By:
Kaylea B.
Checked
By:
Aimee D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Vessel
Identification:
Function:
Operation:
Materials
Handled:
Item
Flash Regenerator
Name:
Item
V-303
Number:
To recover methanol by removing H2S
Continuous
Feed
Stream ID:
Quantity
(kg/hr):
Temperature
(C):
Pressure
(kPa):
Design Data:
18
5.841E+4
Claus
Gas
12
8.625E+4
Date: 5-10-2013
Overhead
Bottoms
26
8.623E+4
25
5.844E+4
-25.8
689.5
Type:
Tray Column
Height:
9.1 m
Diameter:
Materials of Construction:
0.91 m
316 Internals, Carbon Steel
Comments:
161
Done By:
Kaylea B.
Checked
By:
Aimee D.
Synthesis Gas Purification Unit Equipment Specification Sheets
Vessel
Identification:
Function:
Operation:
Materials
Handled:
Item
Hot Regenerator
Name:
Item
V-304
Number:
To recover methanol by removing H2S
Continuous
Feed
25
5.844E+4
Stream ID:
Quantity
(kg/hr):
Overheads
27
4528
Bottoms
31
6.442E+4
Done By:
Checked
By:
Kaylea B.
Aimee D.
Condensed
29R
4075
Stream ID:
Quantity
(kg/hr):
Temperature
(C):
Pressure
(kPa):
Design Data:
Reboil
33R
6428
Date: 5-10-2013
16.5
206.8
Type:
Tray Column
Height:
Diameter:
Materials of Construction:
7.6 m
1.2 m
316 Stainless
Comments:
162
Synthesis Gas Purification Unit Equipment Specification Sheets
Pump and Motor
Identification:
Function:
Operation:
Materials
Handled:
Item
Hot Regenerator
Name:
Bottoms Pump
Item
PNumber:
301
To pump from heat exchanger to absorber
Continuous
Inlet
31
6.442E+4
Stream ID:
Quantity
(kg/hr):
Temperature (
C):
Pressure
Out(kPa):
Design Data:
Date: 5-10-2013
Outlet
31P
6.442E+4
16.7
344.7
Type:
Materials of
Construction:
Volumetric
Flow:
Pressure Change:
Centrifugal
Carbon Steel
Power Required:
4.1 kW
266 gpm
20
Comments:
Done By:
Checked
By:
163
Kaylea
B.
Aimee
D.
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Heat Exchanger
Identification:
Function:
Operation:
Materials
Handled:
Item Name:
Heat Exchanger
Date:
Item Number:
E-400
To exchange energy between hot and cold streams
Continuous
Stream ID:
Quantity
(kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
THOT In (C):
THOT Out (C):
1500
410
TCOLD In (C):
TCOLD Out (C):
Pressure (kPa):
344
403
4/23/2013
Cold in
37
984600
Hot In
W-400
33900
Hot Out
W-401
33900
Cold Out
41
984600
0.4709
0.2896
0.0172
0.1428
0.0795
0.0000
0.0000
0.0000
0.0000
1.0000
0.0000
0.0000
0.0000
0.0000
1.0000
0.4709
0.2896
0.0172
0.1428
0.0795
2000
350
350
2000
Design Data:
Type:
Materials of Construction:
Fixed Shell and Tube
316 Stainless Steel
Heat Duty:
Shell Diameter:
9.0E+07
0.75
kJ/h
m
Tube Diameter:
0.016
m
Heat Transfer Coefficient:
4737
kJ/h m2 oC
Heat Transfer Area:
60.3
m2
Comments: E-400 is used to heat the combined syngas and recycle stream in order to bring the
temperature of the stream up to where it can be sent directly into the syngas to methanol reactor.
Done By:
Aimee D.
Checked By:
164
Mitch G.
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Heat Exchanger
Identification:
Function:
Operation:
Materials Handled:
Item Name:
Heat Exchanger
Date:
Item Number:
E-401
To exchange energy between hot and cold streams
Continuous
Stream ID:
Quantity
(kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
THOT In (C):
THOT Out (C):
710
60
TCOLD In (C):
TCOLD Out (C):
25
100
Pressure
(kPa):
Design Data:
4/23/2013
Cold in
W-402
447400
Hot In
39
984500
Cold Out
W-403
447400
42
984500
0.0000
0.0000
0.0000
0.0000
1.0000
0.3575
0.1731
0.1780
0.2914
0.0000
0.0000
0.0000
0.0000
0.0000
1.0000
0.3575
0.1731
0.1780
0.2914
0.0000
101.3
1640
1640
101.3
Type:
Fixed Shell and Tube
Materials of Construction:
316 Stainless Steel
Heat Duty:
Shell Diameter:
1.15E+09
0.75
kJ/h
m
Tube Diameter:
0.016
m
Heat Transfer Coefficient:
552600
kJ/h m2 oC
Heat Transfer Area:
60.3
m2
Hot Out
Comments: E-401 is used to cool the product stream exiting the syngas to methanol reactor (R-400)
in order to bring the temperature of the stream down to where it can be sent directly into the
vertical flash drum (V-400).
Done By:
Aimee D.
Checked By:
165
Mitch G.
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Heat Exchanger
Identification:
Function:
Operation:
Materials Handled:
Item Name:
Heat Exchanger
Date:
Item Number:
E-402
To exchange energy between hot and cold streams
Continuous
Stream ID:
Quantity (kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
THOT In (C):
THOT Out (C):
1000
500
TCOLD In (C):
TCOLD Out (C):
55.9
400
Pressure (kPa):
4/23/2013
Cold in
46
146500
Hot In
W-405
220600
Hot Out
W-406
220600
Cold Out
47
146500
0.0026
0.0006
0.9931
0.0037
0.0000
0.0000
0.0000
0.0000
0.0000
1.0000
0.0000
0.0000
0.0000
0.0000
1.0000
0.0026
0.0006
0.9931
0.0037
0.0000
110
101.3
101.3
110
Design Data:
Type:
Materials of Construction:
Heat Duty:
Shell Diameter:
Fixed Shell and Tube
316 Stainless
Steel
2.54E+08
kJ/h
0.75
m
Tube Diameter:
0.016
m
Heat Transfer Coefficient:
9227
Heat Transfer Area:
60.32
kJ/h m2
o
C
m2
Comments: E-402 is used to heat the methanol-rich stream exiting the flash column (V-400) in
order to bring the temperature of the stream up to where it can be sent directly into the methanol
to gasoline reactor.
Done By:
Aimee D.
Checked By:
166
Mitch G.
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Vessel
Identification:
Function:
Operation:
Materials
Handled:
Item
Vertical Flash
Date:
Name:
Drum
Item
V-400
Number:
To separate methanol from unreacted synthesis gas
Continuous
Stream ID:
Quantity (kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
Temperature
(C):
Pressure (kPa):
4/23/2013
Inlet
42
984400
Distillate
43
146500
Bottoms
45
837900
0.3575
0.1731
0.1780
0.2914
0.0000
0.4125
0.1998
0.0517
0.3360
0.0000
0.0026
0.0006
0.9931
0.0037
0.0000
60
60
60
1639
1639
1639
Design Data:
Height:
Diameter:
Materials of Construction:
23.5
m
6.7
m
316 Stainless Steel
Comments: Methanol separation consisting only of a flash drum. The temperature is set according
to where methanol condenses at inlet pressure.
Done By:
Checked
By:
167
Aimee D.
Mitch G.
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Compressor
Identification:
Function:
Operation:
Materials
Handled:
Item Name:
Compressor
Date:
4/24/2013
Item Number:
K-400
To compress recycled reactants to syngas to methanol reactor pressure
Continuous
Inlet
40
419800
Outlet
38
419800
0.4121
0.2016
0.0518
0.3346
0.0000
0.4121
0.2016
0.0518
0.3346
0.0000
Centrifuga
l
25100
m3/h
365
kPa
Power Required:
1766
kW
Efficiency:
0.75
Stream ID:
Quantity
(kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
T In (C):
T Out (C):
60
72.8
Pressure
In(kPa):
Pressure
Out(kPa):
Design Data:
1635
2000
Type:
Volumetric
Flow:
Pressure Change:
Comments: K-400 is a centrifugal compressor that compresses recycled unreacted synthesis gas to
the synthesis gas to methanol reactor pressure.
Done
Aimee D.
By:
Checked
Mitch G.
By:
168
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Valve
Identification:
Function:
Operation:
Materials
Handled:
Item
Pressure
Date:
Name:
Valve
Item Number:
Z-400
To separate high pressure and low pressure processes
Continuous
4/23/2013
Stream
45
146500
Stream ID:
Quantity (kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
T In (C):
60.0
T Out (C):
55.9
Pressure
In(kPa):
Pressure
Out(kPa):
Design Data:
1640
0.0026
0.0006
0.9931
0.0037
0.0000
110
Type:
Diaphragm
Pressure Drop:
1530
kPa
Comments: Z-400 separates the high pressure of EV-400 from the low pressure of E-402. The
stream going through this valve contains the methanol product that will be fed into the methanol
to gasoline reactor.
Done By:
Aimee D.
Checked
Mitch G.
By:
169
Synthesis Gas to Methanol Unit Equipment Specification Sheets
Synthesis Gas to Methanol Reactor
Identification:
Function:
Operation:
Materials
Handled:
Item
Heat Exchange
Date:
Name:
Reactor
Item
R-400
Number:
To react purified syngas to produce methanol
Continuous
Stream ID:
Quantity (kg/hr):
Composition:
CO
H2
Methanol
CO2
H2O
T In (C):
T Out (C):
403.0
710.0
Pressure In
(kPa):
Pressure Out
(kPa):
Design Data:
2000
Inlet
41
984500
Outlet
39
984400
0.4709
0.2896
0.0172
0.1428
0.0795
0.3575
0.1731
0.1780
0.2914
0.0000
4/24/2013
1640
Type:
Number of Tubes:
Materials of
Construction:
Horizontal
PFR
5000
316 Stainless
Steel
Length:
Diameter:
5.4
10
m
cm
Volume:
212.058
m3
Comments: R-400 is a horizontal PFR receiving purified synthesis gas in a CO:H2 ratio of 2:1 at 403oC
and 2000 kPa. Using a 50wt%Cu/45wt%ZnO/5wt%Al2O3 catalyst (ρc = 1063 kg/m3, dp = 5.5 mm), the
synthesis gas undergoes multiple synthesis reactions to produce methanol.
Done By:
Checked
By:
170
Aimee D.
Mitch G.
Methanol to Gasoline Unit Equipment Specification Sheet
Methanol to Gasoline Reactor
Identification:
Function:
Operation:
Materials Handled:
Item
Shell and Tube
Date:
Name:
Reactor
Item Number:
R-500
Conversion of methanol to a mixture of gasoline components
Continuous
Stream ID:
Quantity (kgmol/hr):
Composition:
Methanol
Methane
C2
C3
C4
C5
C5+
C6H6
C7H8
C8H10
C9H12
C10H14
DME
Water
CO2
T In (C):
670
T Out (C):
Pressure
In(kPa):
Pressure
Out(kPa):
679.7
125
Comments:
Inlet
Outlet
4570
4370
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.038
0.002
0.004
0.005
0.001
0.003
0.034
0.003
0.010
0.035
0.029
0.001
0.005
0.705
0.125
5/9/2013
105
Type:
Materials of Construction:
2
Heat exchange area (m ):
Pressure Change:
3
Volume (m ):
Length (m):
Shell and Tube Reactor
Stainless Steel
1530
20 kPa
26.81
10
Done by:
Tubes :
695
Tube diameter (cm):
7
Checked by:
Mitch G.
Phil R.
o
R-500 is a shell and tube heat exchanger reactor that receives methanol from R-400 at 400 C
and 125 kPa. Converts methanol to gasoline over a oxalic acid treated 0.5 wt% ZnO, 7 wt% CuO, HZSM5
catalyst.
171
Methanol to Gasoline Unit Equipment Specification Sheet
Compressor
Identification:
Function:
Operation:
Materials
Handled:
Item
Name:
Item Number:
Dowtherm A
Date:
5/9/2013
Compressor
K500
Compresses Dowtherm A to account for pressure drop in the R-500 coolant
system
Continuous
Inlet
Outlet
1205
1205
1.0
1.0
Stream ID:
Quantity
(kg/hr):
Composition:
Dowtherm A
T In (C):
322
Done by:
Mitch G.
T Out (C):
326.8
Checked
by:
Phil R.
Pressure
In(kPa):
Pressure
Out(kPa):
Design Data:
110
135
Type:
Centrifugal Compressor
Materials of Construction:
Stainless Steel
Volumetric
Flow:
Pressure Change:
13.4
m3/s
25 kPa
Power Required:
Comments:
530
hp
Compresses dowtherm A to account for pressure drops in the reactor R-500
coolant loop
172
Methanol to Gasoline Unit Equipment Specification Sheet
Heat Exchanger
Identification:
Function:
Operation:
Materials
Handled:
Stream ID:
Quantity
(kg/hr):
Composition:
Item
Dowtherm A Heat
Name:
Exchanger
Item
E-500
Number:
Cools Dowtherm A
Continuous
Dowtherm
A
Water (sat
liq.)
Water (sat
vap.)
THOT In (C):
THOT Out (C):
397
322
TCOLD In (C):
180
TCOLD Out (C):
180
Pressure
(kPa):
Design Data:
Comments:
Date:
5/9/2013
Cold
in
Hot In
Hot Out
Cold Out
14550
140000
14550
140000
1.0
1.0
1.0
1.0
Done
by:
Checked
by:
1.35 kPa
Type:
Floating Heat
Materials of
Construction:
Shell
Diameter:
Heat Transfer
Coefficient
Number of Tubes
Stainless Steel
Heat Transfer Area:
60 m2
740 mm
2738
kJ/(h*m2*C)
160
Cools Dowtherm A to the temperature required by R-500
173
Mitch G.
Phil R.
Final Separations Specification Sheets
Heat Exchanger
Identification:
Item
Simple
Date: 5-10-2013
Name:
Cooler
Item
E-600
Number:
Function: To cool the stream that is coming from the reactor
Operation: Standard shell and tube with the water in the shell and the separator feed in the tubes
Materials
Gasoline
Handled:
Mixture
Cold in
Hot In
Hot Out
Cold Out
Stream ID:
Cooling
48
49
Hot
water
steam
Quantity
90250
138712
138712
90250
(kg/hr):
Description:
water
gasoline
gasoline
steam
THOT In (C):
THOT Out (C):
400
30
TCOLD In (C):
TCOLD Out (C):
-0.7474
50
Pressure (kPa):
110
100
110
107.5
120
Design Data:
Type:
Shell and Tube
Materials of Construction:
Shell Diameter:
316 Stainless Steel
739mm
Tube Diameter
16 mm
Heat Transfer Coefficient:
Heat Duty:
Heat Transfer Area:
3.139E+5 kJ/(hr*m^2*C)
2.502E+8 kJ/hr
75.4
m^2
Comments: E-600 is used to cool the stream that is coming out of the reaction system and coming
into the separation system with a continuous flow of cooling water and also making steam that can
be transported and used for other heating separations.
Done
Philip
By:
Reinken
Checked
Kaylea
By:
Brase
174
Final Separations Specification Sheets
Heat Exchanger
Identification:
Item
Simple Heater 1
Date: 5-10-2013
Name:
Item
E-601
Number:
Function: To warm the stream to a reasonable temperature for mixing
Operation: Standard shell and tube with the steam in the shell and the recycle stream in the tubes
Materials
Gasoline Mixture
Handled:
Cold in
Hot In
Hot Out
Cold Out
Stream ID:
52
Hot
Cooled
53
Steam
Steam
Quantity
150888
35270
35270
150888
(kg/hr):
Composition:
gasoline
steam
steam
gasoline
THOT In (C):
THOT Out (C):
200
150
TCOLD In (C):
-4
TCOLD Out (C):
20
Pressure (kPa):
107.5
Design Data:
Type:
Shell and Tube
Materials of Construction:
Heat Duty:
Shell
Diameter:
Tube
Diameter
Heat Transfer Coefficient:
Heat Transfer Area:
316 Stainless Steel
3.425E+6 kJ/hr
739 mm
16 mm
348 kJ/(hr*m^2*C)
60.32
m^2
Comments: E-601 is used to heat the recycle to a reasonable temperature to be run through a
compressor and later mix with the feed stream to the three-phase separator.
Done By:
Checked
By:
175
Philip
Reinken
Kaylea
Brase
Final Separations Specification Sheets
Heat Exchanger
Identification:
Item
Simple Heater 2
Date: 5-10-2013
Name:
Item
E-602
Number:
Function: To warm the stream to a reasonable temperature for transportation
Operation: Standard shell and tube with the steam in the shell and the effluent stream in the tubes
Materials
Gasoline
Handled:
Mixture
Cold in
Hot In
Hot Out
Cold Out
Stream ID:
61
Hot
Cooled
62
Steam
Steam
Quantity
32657
85195
85195
32657
(kg/hr):
Composition:
Purged
Steam
Steam
Purged
fuel
fuel
THOT In (C):
THOT Out (C):
200
100
TCOLD In (C):
TCOLD Out (C):
-0.7474
50
Pressure (kPa):
150
Design Data:
Type:
Shell and Tube
Materials of Construction:
Heat Duty:
Shell Diameter:
316 Stainless Steel
3.425
739 mm
Tube Diameter
Heat Transfer Coefficient:
16 mm
348 kJ/(hr*m^2*C)
Heat Transfer Area:
60.32 m^2
Comments: E-602 will be used to heat the effluent gases so that the entire stream is in the vapor
phase and is easier to transport to other processes.
Done By:
Checked
By:
176
Philip
Reinken
Kaylea
Brase
Final Separations Specification Sheets
Vessel
Identification:
Item
Three-Phase Separator
Date: 5-10-2013
Name:
Item
V-600
Number:
Function: To separate the three phases of the products from the reaction stages
Operation: It will have one inlet and three outlets: a vapor, a side drawn organic, and an aqueous
stream
Materials
Gasoline
Handled:
Mixture
Inlet
Outlet
Stream ID:
50
51
56
57
Quantity
289614
177514
(kg/hr):
50941
61158
Composition:
Gasoline
Light gases
Mixture
Gasoline Mixture
Water/Methanol
Temperature
(C):
Pressure (kPa):
-4
107.5
Design Data:
Type:
Three-Phase Separator
Volume:
Length:
Diameter:
Material:
386.5 m^3
18.14 m
5.182 m
316 Stainless Steel
Comments: V-600 will be a three-phase separation vessel with a vapor stream, an organic product
side stream and an aqueous waste stream.
Done By:
Checked
By:
177
Philip
Reinken
Kaylea
Brase
Final Separations Specification Sheets
Distillation Tower
Identification:
Item
Vertical Trayed Distillation
Date: 5-10-2013
Name:
Column
Item
TNumber:
600
Function: To separate contaminants from the waste water so that it can be sent to a treatment
facility
Operation: It will separate the methanol and other contaminants from the waste water
Materials
Gasoline
Handled:
Mixture
Inlet
Outlet
Stream ID:
57
58
59
60
Quantity
61159
298
(kg/hr):
5731
55128
Composition:
Methanol/Water
Light gases
Methanol
Water
Temperature
©:
Pressure
(kPa):
Design Data:
105
135
Number of
Trays:
Volume:
20 trays
Height:
Diameter:
Material:
60 m
1.5 m
316 Stainless Steel
386.5 m^3
Comments: T-600 will be a distillation column that will purify the water enough to send it to a
waste-water treatment facility for further purification.
Done
Philip
By:
Reinken
Checked
Kaylea
By:
Brase
178
Appendix G: Octane Number Calculations
RON
MON
AON
AON*kgmol/h
Kgmol/h
C2
108
108
108
53.82072
0.49834
C3
112
100
106
373.6606
3.5251
C4
113
114
113.
5
323.929
2.854
Pentane
66
62
64
571.936
8.9365
Hexane
25
25
25
3482.25
139.29
Benzene
98
91
94.5
981.7605
10.389
Toluene
124
112
118
5067.982
42.949
Xylene
162
124
143
21670.22
151.54
Trimethylbenzen
e
170
136
153
19538.1
127.7
C10H14
110
110
110
651.618
5.9238
MeOH
133
105
119
505.5715
4.25
52469.44
497.8557
Octane
Number
105.3912
RON is the research octane rating, and MON is the Motor octane rating. These are averaged to obtain
the values in the column labeled AON. These average octane ratings were then multiplied by the molar
flow, these quantities were summed, and divided by the total molar flow to determine the octane rating
of our fuel.
179
Appendix H: UNISIM Workbook for Syngas Purification
180