Integrating Bioprocesses into Industrial
Complexes for Sustainable Development
CO2
Debalina Sengupta
Department of Chemical Engineering, Louisiana State University
Introduction
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Sustainable Development
Overview
Biomass conversion designs
Superstructure formulation
Optimal complex
Case studies
Conclusions
Sustainability
“Sustainable development is development that meets the needs
of the present without compromising the ability of future
generations to meet their own needs.” – Brundtland Report,
United Nations
There are numerous approaches to apply sustainable
development by world organizations, countries and
industries.
Life Cycle Assessment (LCA)
Eco-Efficiency Analysis
Sustainability Indicators: Metrics and Indices
Industrial Ecology
Carbon Dioxide Sequestration
(CCS, bio-sequestration, chemical sequestration)
Total Cost Assessment Methodology (TCA)
(Economic Costs, Environmental Costs,
Societal Costs)
AIChE Total Cost Assessment Methodology
• Methodology developed by an industry group
• Assesses economic, environmental and societal costs
• Detailed report on total cost assessment (Constable et al., 1999).
• Project Team
AD Little (Collab. & Researcher)
DOE
Eastman Chemical
Georgia Pacific
Merck
Owens Corning
SmithKline Beecham (Lead)
Bristol-Myers Squibb
Dow
Eastman Kodak
IPPC of Business Round Table
Monsanto
Rohm and Haas
Sylvatica (TCAce Dev.)
• TCA Users Group created in May 2009. Work is ongoing to
update the costs identified in the report.
Constable, D. et al., “Total Cost Assessment Methodology; Internal Managerial Decision Making Tool”, AIChE, ISBN 0-81690807-9, July ,1999.
Corporate Sustainability
• A company’s success depends on maximizing profit
Profit =  Product Sales –  Raw Material Costs –  Energy Costs
• The profit equation expanded to include environmental costs and societal
costs to meet the “Triple Bottomline” criteria
Triple Bottom Line =  Product Sales
+  Sustainable Credits
–  Raw Material Costs –  Energy Costs
–  Environmental Costs –  Sustainable Costs
Triple Bottom Line =  Profit -  Environmental Costs +  Sustainable (Credits – Costs)
Industries in Louisiana
• Petrochemical complex in the lower Mississippi River Corridor
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Dow
DuPont
BASF
Shell
Exxon
Monsanto
Mosaic
Union Carbide
…. and others
Photo: Peterson, 2000
Objectives of Research
• Identify and design new industrial scale bioprocesses that use
renewable feedstock as raw materials with Aspen HYSYS®
• Construct block models of bioprocesses for optimization
• Integrate new bioprocesses into a base case of existing plants
to form a superstructure of plants (using the chemical
production complex in the Lower Mississippi River Corridor)
• Optimize the superstructure based on triple bottomline
• Obtain the optimal configuration of existing and new plants
(chemical complex optimization)
• Demonstrate use of the superstructure for parametric studies
Overview
• Biomass based processes integrated into a chemical production
complex.
• Utilize carbon dioxide from processes in the integrated complex.
• Assign costs to the Triple Bottomline Equation.
• Mixed Integer Non-Linear Programming problem
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maximize the Triple Bottomline
multiplant material and energy balances
product demand and raw material availability
plant capacities
• Chemical Complex Analysis System used to obtain optimal solution to
the MINLP problem (including Pareto optimal sets)
• Monte Carlo simulation used to determine sensitivity of optimal
solution to price of raw materials and products
Biomass Processes
Biomass conversion processes designed for integration
into the chemical complex
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Fermentation
Anaerobic digestion
Transesterification
Gasification
Algae oil production
Pretreatment of biomass is needed to make feedstock
available for conversion to products
Aspen HYSYS® - Process simulation
Aspen ICARUS Process Evaluator® - Cost Estimation
Glycerol derivatives
Proposed Biomass-Based Complex Extension
Glycerol
1,3- propanediol
Propylene glycol
Natural
Oils
Transesterification
Polyurethane
polyols
FAME or FAEE
Ethanol
Methanol
Sugars
Ethanol derivatives
Fermentation
Ethanol
Ethylene
Ethylene derivatives
C6 Sugars
Starches
Cellulose and
Hemicellulose
Enzyme
Conversion
C5/C6 Sugars
Succinic Acid
Succinic acid derivatives
Butanol
Butanol derivatives
Levulinic Acid
Levulinic acid derivatives
Acid or Enzyme Hydrolysis
Acid dehydration
Carbon
Nanotubes
Single Walled CNT
Ammonia
Ammonia derivatives
Syngas
Gasification
Anaerobic
Biodigestion
Methanol
Methanol derivatives
Acetic Acid
Acetic acid derivatives
CH4
Design Description of Transesterification
4250 kg/hr
Glycerol
Natural
Oils
393 kg/hr
4250 kg/hr
Methanol
Transesterification
Thermodynamic
model
UNIQUAC
Reactants
Methanol
Soybean Oil
Catalyst
1.78% (w/w) Sodium Methylate
in methanol
Products
Methyl Ester
Glycerol
Temperature
60oC
FAME or FAEE
612 kg/hr
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Transesterification
10 million gallons per year 1 of Fatty Acid
Methyl Ester (FAME) produced
Methyl Ester Purification
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FAME is utilized in manufacture of polymers
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Glycerol is used in manufacture of
propylene glycol
1 Design
Wash agents
Water
HCl
Glycerol Recovery and Purification
Purification
Agents
NaOH
Water
HCl
based on “A process model to estimate biodiesel production costs”,M.J. Haas et al., Bioresource Technology 97 (2006) 671-678
HYSYS Design of Transesterification Process
Transesterification
Reaction
Methyl ester purification
Glycerol recovery and purification
Design description of Propylene Glycol
246 kg/hr
Hydrogen, 200oC, 200 psi
Glycerol
Propylene Glycol
9,300 kg/hr
15,000 kg/hr
• The design is based on a process for
hydrogenation of glycerol to propylene
glycol 1
• ~65,000 metric ton of propylene glycol is
produced per year2
1 Design
2
Hydrogenolysis
Thermodynamic model
UNIQUAC
Reactants
Glycerol
Hydrogen
Catalyst
Copper Chromite
Products
Propylene Glycol
Water
Temperature
200oC
Pressure
200 psi
based on experimental results from Dasari, M. A. et al. 2005, Applied Catalysis, A: General, Vol. 281, p. 225-231.
Capacity based on Ashland/Cargill joint venture of process converting glycerol to propylene glycol
HYSYS Design of Glycerol to Propylene Glycol
Hydrogenolysis Reaction
Purification of Propylene Glycol
Process Flow Design to
Block Flow Model for Optimization
S3001
S3020
S3002
S3003
S3021
TRANSESTERIFICATION
S3004
S3022
S3005
S3023
S3006
Biomass-Based Complex Extension
Base Case of Plants in the Lower Mississippi River Corridor
Plants in the Base Case
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Ammonia
Nitric acid
Ammonium nitrate
Urea
UAN
Methanol
Granular triple super phosphate
MAP & DAP
Sulfuric acid
Phosphoric acid
Acetic acid
Ethylbenzene
Styrene
Integrated Chemical Production Complex
Hydrogen,CO2
Biomass Complex
Air, Methanol, Ammonia
Base Case Complex
Superstructure
Profit
Chemicals like
methylamines,
methanol, acetic
acid etc. from CO2
CO2
Algae growth for
use as biomass
Triple Bottom Line =  Profit -  Environmental Costs +  Sustainable (Credits – Costs)
Superstructure
Continuous Variables: 969
Integer Variables: 25
Equality Constraints: 978
Inequality Constraints: 91
Plants in Base Case
(blue)
Ammonia
Nitric acid
Ammonium nitrate
Urea
UAN
Methanol
Granular triple super phosphate
(GTSP)
MAP and DAP
Contact process for sulfuric acid
Wet process for phosphoric acid
Acetic acid – conventional method
Ethyl benzene
Styrene
Power generation
Plants Added to Form the Superstructure
Bioprocesses and CO2 consumption by Algae (green)
Fermentation ethanol (corn stover)
Fermentation ethanol (corn)
Anaerobic Digestion to acetic acid (corn stover)
Algae Oil Production
Transesterification to FAME and glycerol (soybean oil and
algae)
Gasification to syngas (corn stover)
Ethylene from dehydration of ethanol
Propylene glycol from glycerol
CO2 consumption for Chemicals (red)
Methanol – Bonivardi, et al., 1998
Methanol – Jun, et al., 1998
Methanol – Ushikoshi, et al., 1998
Methanol – Nerlov and Chorkendorff, 1999
Ethanol
Dimethyl ether
Formic acid
Acetic acid - new method
Styrene - new method
Methylamines
Graphite
Hydrogen/Synthesis gas
Propylene from CO2
Propylene from propane dehydrogenation
Choice for phosphoric acid production and SO2 recovery
(yellow)
Electric furnace process for phosphoric acid
Haifa process for phosphoric acid
SO2 recovery from gypsum waste
S and SO2 recovery from gypsum waste
Optimization Problem
Maximize: Triple Bottom Line
Triple Bottom Line =
 Profit
-  Environmental Costs
+ Sustainable (Credits – Costs)
Subject to:
Multiplant material and energy balance
Product demand
Raw material availability
Plant capacities
Optimal structure obtained by using Global Optimizers
Optimal Solution
Existing Plants in the Optimal
Structure
Ammonia
Nitric acid
Ammonium nitrate
Urea
UAN
Methanol
Granular triple super phosphate
(GTSP)
MAP and DAP
Contact process for Sulfuric acid
Wet process for phosphoric acid
Power generation
Existing Plants Not in the Optimal
Structure
Acetic acid
Ethylbenzene
Styrene
New Plants in the Optimal Structure
Fermentation to ethanol (corn)
Bio-ethylene from dehydration of bio-ethanol
Transesterification to FAME and glycerol (soy oil
and algae)
Algae oil production Bio-propylene glycol from
glycerol
Gasification to syngas (corn stover)
Formic acid
Graphite
Propylene from CO2
Propylene from propane dehydrogenation
New Plants Not in the Optimal Structure
Fermentation to ethanol (corn stover)
Anaerobic Digestion to acetic acid (corn stover)
Methanol – Bonivardi, et al., 1998
Methanol – Jun, et al., 1998
Methanol – Ushikoshi, et al., 1998
Methanol – Nerlov and Chorkendorff, 1999
Methylamines (MMA and DMA)
Ethanol
Dimethyl ether
Hydrogen/synthesis gas
Acetic acid – new process
Styrene - new method
Electric furnace process for phosphoric acid
Haifa process for phosphoric acid
SO2 recovery from gypsum waste
S and SO2 recovery from gypsum waste
Comparison of Base Case with Optimal Structure
(Triple Bottomline)
Base Case
Million $/year
2,026
Optimal Structure
Million $/year
2,490
Economic Costs
697
516
Raw Material Costs
685
470
12
46
Environmental Costs
457
313
Sustainable Credits(+)/Costs(-)
-18
-10
Triple Bottomline
854
1,650
Income from Sales
Utility Costs
Comparison of Base Case with Optimal Structure
(Energy Requirement)
Base Case (TJ/yr)
Optimal Structure (TJ/yr)
Ammonia
3,820
3,820
Methanol
2,165
1,083
-14,642
-14,642
5,181
5,181
Corn Ethanol
na
4,158
Fatty Acid Methyl Esters
na
1,293
4,374
5,512
898
6,405
Sulfuric acid
Wet process phosphoric acid
Others
Total Energy
Comparison of CO2 use in Base Case and Optimal Structure
Base Case Emission (million metric tons per year)
: 0.75-0.14 = 0.61
Optimal Structure Emission (million metric tons per year) : 1.07-1.07 = 0
Pure Carbon Dioxide Consumption
1.2
1.2
1
1
0.32
0.8
0.6
1.07
0.4
0.75
0.75
million metric tons per year
million metric tons per year
Pure Carbon Dioxide Sources
0.16
0.8
0.6
1.07
0.4
0.2
0.2
0
0
0.14
Base Case
Optimal Structure
Pure CO2 (ammonia plant)
Pure CO2 (bioprocesses)
0.84
Base Case
0.07
Optimal Structure
Pure CO2 (new CO2 chemicals)
Pure CO2 (algae)
Pure CO2 (existing chemical plants)
Multicriteria Optimization Problem
Maximize:
w1P+w2S
P =  Product Sales –  Economic Costs –  Environmental Costs
S =  Sustainability (Credits – Costs)
w1 + w 2 = 1
Subject to:
Multiplant material and energy balance
Product demand
Raw material availability
Plant capacities
Pareto Optimal Solutions
Sustainable Credit(+)/Cost(-)
(million dollars per year)
30
25
20
15
10
P=$1,194 M/yr
S=$26 M/yr
w1: 0.000-0.003
P=$1,346 M/yr
S=$25.6 M/yr
w1: 0.004-0.035
P=$1,369 M/yr
S=$24.7 M/yr
w1: 0.036-0.106
P=$1,660 M/yr
S=-$ 9.98 M/yr
w1: 0.107-1.000
5
0
-5
-10
-15
1100
1200
1300
1400
1500
Profit (million dollars per year)
1600
1700
Sensitivity of Optimal Solution
Cumulative Probability of Triple Bottomline
100%
Cumulative Probability (%)
90%
80%
$2,150 million/yr
70%
60%
50%
40%
30%
20%
10%
80%
$1,650 million/yr
20%
0%
Triple Bottomline (million dollars per year)
20% probability of Triple Bottomline equal or below $1,650 million per year
80% probability of Triple Bottomline equal or below $2,150 million per year
Case Studies with Superstructure
Case Study
Case Study I – Superstructure
without carbon dioxide use
Case Study II – Effect of sustainable
costs and credits on the triple
bottomline
Case Study III – Effect of algae oil
production costs on the triple
bottomline
Case Study IV – Multicriteria
optimization using 30% oil content
algae production and sustainable
costs/credits
Case Study V – Effect of corn and
corn stover costs and number of
corn ethanol plants on the triple
bottomline
Result
Triple bottomline decreased to $984 million per year in
optimal structure without CO2 use from $1,650 million per
year in optimal structure with CO2 use.
The highest triple bottomline was $1,700 million per year for
CO2 cost of $5 and credit of $50 per MT/ton and the lowest
was $1,652 million per year for CO2 cost of $125 and credit of
$25 per MT/ton.
Comparative study of algae oil production costs based on
strain (30% or 50% oil content) and technology (HP,LP,AP).
High performance plant for 30% and high and average
performance plant for 50% oil content strain were included in
optimal solutions. Algae production costs comparable to
soybean oil purchased price were included in optimal
structure.
30% oil content high performance and low performance algae
oil production with $125/MT CO2 cost and $25/MT CO2 credit.
Pareto optimal sets obtained for multicriteria of maximizing
profit and sustainable credits.
Corn stover is competitive when corn price is high.
Constraints on corn ethanol plants showed that decreasing the
number of corn ethanol plants decreased the triple
bottomline, as corn stover ethanol plants used more energy
and emitted impure CO2.
Summary
• Extend the Chemical Production Complex in the Lower Mississippi River
Corridor to include:
Biomass feedstock based chemical production
CO2 utilization from the complex
• Obtained the process designs and constraints
• Assigned Triple Bottomline costs:
Economic costs
Environmental costs
Sustainable credits and costs
• Solved Mixed Integer Non Linear Programming Problem with Global
Optimization Solvers to obtain optimal solution (including Pareto optimal
sets)
• Uses Monte Carlo Analysis to determine sensitivity of the optimal solution
Conclusions
• Demonstrated a new methodology for the integration of bioprocesses in an existing
industrial complex producing chemicals.
– Five processes designed in Aspen HYSYS® and cost estimations performed in
Aspen ICARUS®.
– Three processes converted biomass to chemicals, and two processes converted
the bioproducts into ethylene and propylene chain chemicals.
– Fourteen bioprocess blocks were integrated into a base case of plants in the
Lower Mississippi River corridor to form a superstructure.
• Optimal configuration was determined by optimizing a triple bottom line profit
equation.
– Renewable resources as feedstock and carbon dioxide utilization had the triple
bottomline profit increase by 93% from the base case.
– Algae oil production and other chemical processes consumed all the pure carbon
dioxide emitted from the complex.
– Sustainable costs to the society decreased by 44% due to complete consumption
of pure CO2.
– Total energy required by the optimal complex was 6,405 TJ/yr.
– Total utility costs for the complex increased to $46 million per year from $12
million per year in the base case.
Conclusions
• Multicriteria optimization of the complex gave Pareto optimal solutions . A range of
profit and sustainable credits/costs was obtained for a range of weights on the
multiple objectives.
• Monte Carlo simulations of the complex gave sensitivity of triple bottomline with
respect to price of raw materials and products.
• Five case studies demonstrated the use of chemical complex optimization for
sustainability analysis.
• The methodology could be applied to other chemical complexes in the world for
reduced emissions and energy savings.
Recommendations
• The methodology can be applied to other chemical complexes of the world.
Plants in the Gulf Coast Region (Texas, Louisiana, Mississippi, Alabama) could
be included in the base case.
• Raw material availability constraints related to crop cycles and
transportation costs can be included in the model (supply-chain).
• Price elasticities can be used as leading indicators to estimate future prices
of chemicals in the complex and have optimization over time periods.
• HYSYS designs for algae oil production and gasification processes can be
made when more data becomes available for these processes.
Acknowledgements
• Dr. R. Pike, Dr. F.C. Knopf, Dr. J. Romagnoli, Dr. K.T.
Valsaraj and Dr. J. Dowling
• The Cain Department of Chemical Engineering, LSU for
support
• Tom Hertwig for industrial expertise
• Lise Laurin (Earthshift) for Total Cost Assessment
Methodology
• Aimin Xu and Sudheer Indala for the base case