POLITECNICO DI MILANO
School of Industrial Engineering
Department of Energy
SUPERCRITICAL WATER GASIFICATION OF BIOMASS:
KINETIC APPROACH & PROCESS SIMULATION
Relatore:
Professor Emanuele Martelli
Co-relatore: Professor Wiebren de Jong
(Delft University of Technology)
Master of Science Thesis of:
Guchan Yapar - 797694
Academic Year 2014-2015
To the coal miners of my country and the
world; I hope one day you will not have to risk
your lives to give us a light'
∗to my beloved Mother and nephew Aslan.
ACKNOWLEDGEMENTS
I would like to first give my thanks to Prof. Emanule Martelli,
supervisor of my thesis work in Politecnico di Milano, for his great
help even from an other country.
I am very grateful to my supervisor in Delft University of
Technology, Prof. Wiebren de Jong, for all his support and effort he
spent for the success of this work.
Special thanks to my daily supervisor Onursal Yakaboylu who
is a great science person and a friend. Wishing you the best of luck.
I would like to say thank you, to my friends in Italy, Turkey and
new ones in Netherlands; you have made this journey more
memorable.
ABSTRACT
Thermochemical biomass conversion is a method to transform stored
energy to higher energy density solid, liquid or gas fuels by chemically
decomposing the high molecular weight biopolymers. Biomass derived green gas
has been worked extensively with the aim of taking over the place of fossil based
combustible gas, for people's self-sufficiency in energy consumption along the
earth and preserving a sustainable environment. Biomass gasification at high
temperature - low pressure conditions targets to produce a combustible gas,
abundant of H2 (high calorific gas) and CO (reactant of further shift reactions).
Stated technology is able to scale up to 600 MW energy production capacities for
various biomass or residues but with different thermal efficiencies due to the
external heat requirement. Among all other physical and chemical properties,
moisture content of the biomass may result in thermal efficiency issues and
parallel to this power production decreases.
Supercritical Water Gasification (SCWG) allows the thermochemical
conversion of wet biomass in hydrothermal media. High-pressure biomass slurry
can be kept in aqueous media until the critical point of water. Right above nearcritical temperature level (∼330 ℃) hydrogen bond number decreases and
water molecule becomes more apolar, as a consequence water solubility
increases allowing one-phase homogenous reactions; furthermore, ion
concentration in the hydrothermal media increases, which favors the acid or
base catalyzed reactions. Thermo dynamical benefits have aroused the curiosity
of several science groups for the last 20 years, providing modeling and
experiment studies on supercritical water gasification of biomass (SCWG).
However, detailed kinetic modeling of the process for lignocellulosic biomass is
missing, strongly needed for further technology improvements.
In this work, review and evaluation of the biomass kinetic approach has
been performed with the aim of proposing reaction network and rate
parameters for three main biomass structural compounds; cellulose,
hemicellulose and lignin. AspenPlus 8.3 engineering software package has been
aided for the simulation of the tubular reactors, integrated with the compiled
kinetic data from literature and proposed network. Validation results show that
reactor simulation has a good accuracy of predicting gasification efficiency,
carbon efficiency and gas yield for a range of residence time (15-47 s), pressure
(20-30 MPa) and biochemical composition (mostly agricultural residues).
Reactor simulation validations showed the possibility of inserting consecutive
reactors to a simplified SCWG process simulation. Process scheme including 3
tubular flow reactors and auxiliary units has been designed; according to which,
carbonmonoxide low product gas and high gasification efficiency (4,45 % ,
92,24%, respectively) have been achieved. The results of this work show that
kinetic model developed for lignocellulosic biomass, based on the literature data
is able to predict gasification efficiency, intermediate compound composition,
gas composition and thermal energy output for different operating conditions
and reactor lengths.
TABLE OF CONTENTS
1
INTRODUCTION ......................................................................................................................... 1
1.1 BACKGROUND ........................................................................................................................................ 1
1.2 BIOMASS CONVERSION TECHNOLOGIES-PLACEMENT OF HYDROTHERMAL BIOMASS
GASIFICATION ................................................................................................................................................... 2
1.3 SUPERCRITICAL WATER ...................................................................................................................... 5
1.4 SCWG OF BIOMASS .............................................................................................................................. 7
1.4.1 Main Research Groups and Experiments on SCWG ..........................................................9
1.5 REACTOR SYSTEMS .............................................................................................................................12
1.6 SCOPE AND OBJECTIVES OF THE STUDY ..........................................................................................14
2 LITERATURE REVIEW OF BIOMASS DEGRADATION IN HYDROTHERMAL
MEDIA: REACTION MECHANISM AND KINETICS ..................................................................16
2.1 CELLULOSE DECOMPOSITION ...........................................................................................................17
2.1.1 Glucose Decomposition .............................................................................................................. 20
2.2 HEMICELLULOSE DECOMPOSITION ..................................................................................................23
2.3 LIGNIN DECOMPOSITION ...................................................................................................................25
3
PROPOSED REACTION PATHWAY .....................................................................................31
3.1 Cellulose Pathway: ........................................................................................................................... 32
3.1.1 Decomposition of Cellobiose .................................................................................................... 32
3.1.2 Reactions of Glucose-Fructose decomposition products ............................................ 33
3.2 Hemicellulose Pathway.................................................................................................................. 35
3.3 LIGNIN PATHWAY:..............................................................................................................................37
3.4 ACID DECOMPOSITION PATHWAY: ..................................................................................................39
3.5 KINETICS DATA AND ARRHENIUS PARAMETERS...........................................................................41
4 INTEGRATION OF KINETIC DATA SET TO THE REACTOR SIMULATION- TEST
RUN & VALIDATION .......................................................................................................................52
4.1 INTEGRATION OF KINETIC DATA SET TO THE ASPENPLUSTM SIMULATION .............................52
4.2 RESULTS OF THE REACTOR TEST-RUN ...........................................................................................54
4.3 VALIDATION OF THE SIMULATION ...................................................................................................59
4.3.1 Effect of Residence Time ............................................................................................................ 61
4.3.2 Effect of Pressure .......................................................................................................................... 64
4.3.3 Effect of Feedstock........................................................................................................................ 66
5
SENSITIVITY ANALYSIS .........................................................................................................69
5.1 SENSITIVITY ANALYSIS FOR REACTOR HEATING RATE EFFECT: ...............................................69
5.2 SCWG PROCESS SCHEME BUILD-UP & SENSITIVITY ANALYSIS FOR PARAMETER
VARIATIONS ON SCWG PLANT SIMULATION ............................................................................................77
5.2.1 Design Parameters and Unit Selections ............................................................................. 77
5.2.2 SCWG OF CELLULOSIC BIOMASS PLANT PROCESS SIMULATION ...........................................80
5.2.3 Effect of Maximum Temperature .......................................................................................... 81
5.2.4 Effect of Biomass Load ............................................................................................................... 82
5.2.5 Effect of Biomass Type................................................................................................................ 84
CONCLUSIONS AND FURTHER DEVELOPMENTS ..................................................................87
REFERENCES:....................................................................................................................................89
ANNEXES ............................................................................................................................................95
A- SUBCRITICAL REGION REACTIONS ....................................................................................................95
B- SUPERCRITICAL REGION REACTIONS ................................................................................................96
C- RGIBSS REACTION STOICHIOMETRIES (FOR LIGNIN PATHWAY) ..................................................98
D- COMPONENT LIST ................................................................................................................................98
FIGURE 1.1 APPLICATION OPTION SCHEME OF SCWG PROCESS OUTLET [7] .................................................................. 4
FIGURE 1.2 PHASE DIAGRAM OF WATER [8].......................................................................................................................... 5
FIGURE 1.3 PROPERTIES OF SUPERCRITICAL WATER [10].................................................................................................. 6
FIGURE 1.4 SIMPLIFIED PROCESS SCHEME OF THE PDU FOR SCWG OF BIOMASS OPERATED AT UNIVERSITY OF
TWENTE [7]...................................................................................................................................................................13
FIGURE 2.1 CELLOBIOSE DECOMPOSITION PATHWAY: 300 ℃ < T < 400 ℃, 25 < P < 40 MPA [51] ..........17
FIGURE 2.2 -LN(1 - X) VERSUS RESIDENCE TIME FOR CELLOBIOSE DECOMPOSITION [51] .........................................18
FIGURE 2.3 ARRHENIUS PLOT OF THE RATE CONSTANT OF CONVERSION OF MICROCRYSTALLINE CELLULOSE IN
WATER (290 C – 400 OC, 25 MPA), [50]...............................................................................................................19
FIGURE 2.4 MECHANISM OF GLUCOSE DECOMPOSITION IN SUB- AND SUPERCRITICAL WATER: 300 ℃ < T < 400
℃, < P < 30 MPA [53]............................................................................................................................................21
FIGURE 2.5 GLUCOSE DECOMPOSITION PATHWAY IN SUB- AND SUPERCRITICAL WATER: 300 ℃ < T < 460 ℃
, P = 25 MPA [3]. .......................................................................................................................................................22
FIGURE 2.6 D-XYLOSE DECOMPOSITION IN HOT COMPRESSED WATER: 180℃ < T < 220℃, P = 10 MPA [56]
..........................................................................................................................................................................................23
FIGURE 2.7 KINETIC MODEL REACTION MECHANISM FOR DECOMPOSITION OF XYLOSE IN SUPERCRITICAL WATER
450℃ < T < 650℃, P = 25 MPA [57] ............................................................................................................24
FIGURE 2.8 D-XYLOSE DECOMPOSITION AND GASIFICATION PATH IN SUPERCRITICAL WATER: KINETIC MODEL
REACTION MECHANISM FOR DECOMPOSITION OF XYLOSE IN SUPERCRITICAL WATER 450℃ < T < 650℃
, P = 25 MPA [57] .....................................................................................................................................................24
FIGURE 2.9 GC-MS CHROMATOGRAM FOR SOLUBLE PRODUCTS OF GUAIACOL DECOMPOSITION IN SUPERCRITICAL
WATER [59] ...................................................................................................................................................................25
FIGURE 2.10 SIMPLE REACTION PATHWAY FOR GUAIACOL (LIGNIN MODEL COMPONENT) IN SUPERCRITICAL
WATER [59] ...................................................................................................................................................................26
FIGURE 2.11 SIMPLE REACTION PATHWAY OF CATECHOL DECOMPOSITION IN NEAR- AND SUPERCRITICAL WATER
[60] .................................................................................................................................................................................27
FIGURE 2.12 CATECHOL FORMATION FROM GUAIACOL IN SUB- AND SUPERCRITICAL WATER [61] .........................28
FIGURE 2.13 PHENOL FORMATION FROM GUAIACOL AND CATECHOL IN SUB- AND SUPERCRITICAL WATER [61]..29
FIGURE 2.14 REACTION PATHWAY OF GUAIACOL UNDER HYDROTHERMAL CONDITIONS: 300℃ < T < 450℃
, P = 25 MPA [61] .....................................................................................................................................................30
FIGURE 2.15 REACTION SCHEME OF PHENOL BENZENE DECOMPOSITION IN SUPERCRITICAL WATER: 300℃ <
T < 450℃, P = 25 MPA [39] ...............................................................................................................................30
FIGURE 3.1 REACTION SCHEME OF CELLOBIOSE IN NEAR CRITICAL WATER: K1, K2, K3, KGE.G, KGG.G FROM [51], KG.E,
KG.A, KG.GLY, KG.F, KG.A, KF.E, KF.GLY FROM [53], KGLY.DIH, KDIH.GLY, KGLY.P, KDIH.P FROM [22], KG.5, KF.5, KF.FU , KFU.CH FROM
[3]....................................................................................................................................................................................33
FIGURE 3.2 INTERMEDIATE DECOMPOSITION SCHEME KF.ACID, KP.ACID, KA.ACID, KE.ACID [54]; K5.LF, K5.FF [62]
..........................................................................................................................................................................................34
FIGURE 3.3 DECOMPOSITION SCHEME OF D-XYLOSE IN SUBCRITICAL WATER [56] .....................................................35
FIGURE 3.4 D-XYLOSE DECOMPOSITION AND GASIFICATION SCHEME IN SUPERCRITICAL WATER
KXY.FU,KXY.WSHS,KFU.WSHS,KAA.GA FROM [57] ......................................................................................................................36
FIGURE 3.5 GUAIACOL DECOMPOSITION SCHEME IN SUBCRITICAL WATER [61] ...........................................................37
FIGURE 3.6 GUAIACOL DECOMPOSITION SCHEME IN SUPERCRITICAL WATER KGU.CH, KHU.GA, KGU.OC, KGU.C,
KGU.T, KC.OC, KT.CH FROM [61]; KC.T, KP.C, KP.T, KP,GA, KP.CH, KT.B, KB.T, KB.P, KB.GA, KB.NA, KNA.CH,
KB.CH FROM [39] ..........................................................................................................................................................38
FIGURE 3.7 ORGANIC ACID GASIFICATION SCHEME IN SUPERCRITICAL WATER. KFA.GA1, KFA.GA2 [63]; KAA.GA
[64]; KWSHS.GA, KPA.GA, KMF.AA [57]; LACTIC ACID REACTIONS [65] ............................................................39
FIGURE 3.8 ARRHENIUS RELATION OF GLUCOSE TO GLYCERALDEHYDE DECOMPOSITION REACTION ........................43
FIGURE 4.1 SIMULATION RESULTS OF CELLOBIOSE DECOMPOSITION PRODUCTS IN ISOTHERMAL SUBCRITICAL
REACTOR AT 370 ℃.....................................................................................................................................................55
FIGURE 4.2 SIMULATION RESULTS OF GLUCOSE DECOMPOSITION PRODUCTS IN ISOTHERMAL SUBCRITICAL
REACTOR AT 370 ℃.....................................................................................................................................................56
FIGURE 4.3 SIMULATION RESULTS OF XYLOSE DECOMPOSITION PRODUCTS IN ISOTHERMAL SUBCRITICAL REACTOR
AT 370 ℃ ......................................................................................................................................................................56
FIGURE 4.4 SIMULATION RESULTS OF GUAIACOL DECOMPOSITION PRODUCTS IN ISOTHERMAL SUBCRITICAL
REACTOR AT 370 ℃.....................................................................................................................................................57
FIGURE 4.5 SIMULATION RESULTS OF ORGANIC ACID DECOMPOSITION PRODUCTS IN ISOTHERMAL SUBCRITICAL
REACTOR AT 370 ℃.....................................................................................................................................................57
FIGURE 4.6 SIMULATION RESULTS OF XYLOSE DECOMPOSITION PRODUCTS IN ISOTHERMAL SUPERCRITICAL
REACTOR AT 650 ℃.....................................................................................................................................................58
TABLE OF FIGURES
FIGURE 4.7 TEMPERATURE PROFILE IN SUBCRITICAL REACTOR ......................................................................................59
FIGURE 4.8 TEMPERATURE PROFILE IN SUPERCRITICAL REACTOR ..................................................................................60
FIGURE 4.9 SCHEME OF THE EXPERIMENTAL SETUP USED IN LU’S WORK [9] ...............................................................61
FIGURE 4.10 EFFECT OF RESIDENCE TIME ON PRODUCT GAS MOLAR FLOW...................................................................62
FIGURE 4.11 COMPARISON BETWEEN SIMULATION RESULTS GE, CE AND EXPERIMENTAL RESULTS GE*, CE* FOR
RESIDENCE TIME BETWEEN 15 AND 47 SECONDS ...................................................................................................63
FIGURE 4.12 COMPARISON BETWEEN PRODUCT YIELD RESULTS OF THE SIMULATION AND EXPERIMENTAL RESULTS
(*), FOR RESIDENCE TIME BETWEEN 15 AND 47 SECONDS ...................................................................................64
FIGURE 4.13 COMPARISON BETWEEN SIMULATION RESULTS GE, CE AND EXPERIMENTAL RESULTS GE*, CE* FOR
RESIDENCE REACTOR PRESSURE BETWEEN 200 AND 300 BARS ..........................................................................65
FIGURE 4.14 COMPARISON BETWEEN PRODUCT YIELD RESULTS OF THE SIMULATION AND EXPERIMENTAL RESULTS
(*), FOR REACTOR PRESSURE BETWEEN 200 AND 300 BARS ...............................................................................66
FIGURE 4.15 COMPARISON BETWEEN SIMULATION RESULTS GE, CE AND EXPERIMENTAL RESULTS GE*, CE* FOR
DIFFERENT FEEDSTOCK; RICE STRAW, PEANUT SHELL, CORN STALK, CORN COB AND WOOD SAWDUST. ........67
FIGURE 5.1 TEMPERATURE PROFILE IN SUBCRITICAL REACTOR FOR THE 1ST SCENARIO .............................................71
FIGURE 5.2 TEMPERATURE PROFILE IN SUPERCRITICAL REACTOR FOR THE 1ST SCENARIO ........................................71
FIGURE 5.3 TEMPERATURE PROFILE IN SUBCRITICAL REACTOR FOR THE 2ND SCENARIO ............................................72
FIGURE 5.4 TEMPERATURE PROFILE IN SUPERCRITICAL REACTOR FOR THE 2ND SCENARIO ........................................72
FIGURE 5.5 TEMPERATURE PROFILE IN SUBCRITICAL REACTOR FOR THE 3RD SCENARIO .............................................73
FIGURE 5.6 TEMPERATURE PROFILE IN SUPERCRITICAL REACTOR FOR THE 3RD SCENARIO ........................................73
FIGURE 5.7 GE AND CE VALUES OF 1ST 2ND AND 3RD SCENARIOS ......................................................................................74
FIGURE 5.8 CARBON MOLE RATIO OF UNCONVERTED LIQUID IN THE EFFLUENT, CARBON (UNC.LIQ)% AND MOLAR
FLOWRATE OF CARBON CONTAINED IN CHAR FOR 1ST 2ND AND 3RD SCENARIOS...................................................75
FIGURE 5.9 PRODUCT GAS COMPOSITION VALUES FOR FIRST, SECOND AND THIRD SCENARIOS ..................................76
FIGURE 5.10 SIMPLIFIED HEAT EXCHANGE UNIT SCHEME.................................................................................................78
FIGURE 5.11 GE AND CE RESULTS OF THE PROCESS SIMULATION FOR TMAX= 650℃, 600 ℃, 550 ℃ ..................81
FIGURE 5.12 BIOMASS LOAD EFFECT ON GE AND CE ........................................................................................................82
FIGURE 5.13 BIOMASS LOAD EFFECT ON THERMAL ENERGY SUPPLIED AND NET THERMAL ENERGY PRODUCTION
RATE ................................................................................................................................................................................83
FIGURE 5.14 BIOMASS LOAD EFFECT ON SPECIFIC THERMAL ENERGY SUPPLIED AND NET THERMAL ENERGY
PRODUCTION RATE ........................................................................................................................................................84
FIGURE 5.15 BIOMASS TYPE EFFECT ON GE, CE AND CGE ..............................................................................................85
FIGURE 5.16 BIOMASS TYPE EFFECT ON PRODUCT GAS COMPOSITIONS..........................................................................86
TABLE 1.1 PROXIMATE ANALYSIS AND CALORIFIC VALUES OF PIG MANURE, ALGAE, COW MANURE AND RICE STRAW
SAMPLES [5]..................................................................................................................................................................... 3
TABLE 1.2 SELECTED SCWG RESEARCH GROUPS [25] ....................................................................................................... 9
TABLE 3.1 CELLOBIOSE DECOMPOSITION RATE CONSTANTS ............................................................................................41
TABLE 3.2 GLUCOSE DECOMPOSITION RATE CONSTANTS ..................................................................................................42
TABLE 3.3ARRHENIUS PARAMETERS OF CELLOBIOSE AND GLUCOSE DECOMPOSITION ...............................................43
TABLE 3.4 GLUCOSE DECOMPOSITION RATE CONSTANTS CONTINUED [3] .....................................................................44
TABLE 3.5 ARRHENIUS PARAMETERS OF GLUCOSE DECOMPOSITION CONTINUED........................................................44
TABLE 3.6 XYLOSE DECOMPOSITION RATE CONSTANTS IN SUBCRITICAL WATER ..........................................................45
TABLE 3.7 ARRHENIUS PARAMETERS OF D-XYLOSE DECOMPOSITION SUBCRITICAL WATER.......................................45
TABLE 3.8 ARRHENIUS PARAMETER OF D-XYLOSE DECOMPOSITION SUPERCRITICAL WATER ....................................45
TABLE 3.9 LIGNIN DECOMPOSITION RATE CONSTANTS SUB- AND SUPERCRITICAL WATER [61] ...............................46
TABLE 3.10 ARRHENIUS PARAMETERS OF GUAIACOL DECOMPOSITION SUBCRITICAL WATER ...................................47
TABLE 3.11 ARRHENIUS PARAMETERS OF GUAIACOL DECOMPOSITION SUPERCRITICAL WATER ..............................47
TABLE 3.12 PHENOL- BENZENE DECOMPOSITION REACTION RATE CONSTANTS SUPERCRITICAL [39] ....................48
TABLE 3.13 ARRHENIUS PARAMETER OF PHENOL-BENZENE DECOMPOSITION IN SUPERCRITICAL WATER .............48
TABLE 3.14 ARRHENIUS PARAMETERS OF FORMIC ACID DECOMPOSITION [63]...........................................................49
TABLE 3.15 ARRHENIUS PARAMETERS OF ACETIC AND PROPIONIC ACID DECOMPOSITION ........................................49
TABLE 3.16 LACTIC ACID DECOMPOSITION REACTION RATE CONSTANTS [65] ............................................................50
TABLE 3.17 ARRHENIUS PARAMETERS OF LACTIC ACID DECOMPOSITION SUPERCRITICAL WATER ...........................51
TABLE 4.1SIMULATION SET PARAMETERS FOR PRELIMINARY RUN .................................................................................54
TABLE 4.2 SIMULATION OPERATING PARAMETERS; COMPARISONS FOR VARYING RESIDENCE TIME .........................61
TABLE 4.3 SIMULATION OPERATING PARAMETERS; COMPARISONS FOR VARYING PRESSURE .....................................64
TABLE 4.4 BIOCHEMICAL AND ASH MASS COMPOSITIONS OF DRY BIOMASS SAMPLES USED IN THE SIMULATION ....66
TABLE 5.1 SIMULATION SET PARAMETERS ..........................................................................................................................70
TABLE 5.2 TUBULAR REACTOR SIZING PARAMETERS .........................................................................................................79
TABLE 5.3 BASE CASE SIMULATION RESULTS ......................................................................................................................80
NOMENCLATURE
Roman
M
𝑤𝑎𝑡𝑒𝑟
∆𝐻𝑣𝑎𝑝
Pc
Tc
V
S
t
k
ks
A
Ea
Kw
r
R
T0
Ci
a, b
c
𝑚𝑔,𝑜
𝑚𝑏𝑖𝑜,𝑑𝑟𝑦
𝑛𝑐,𝑔
𝑛𝑐,𝑏𝑖𝑜
𝑛𝑖
𝑌𝑖
∆𝑇𝐿𝑀
𝑈
ℎ𝑖
𝑘𝑤𝑎𝑙𝑙
𝑟
𝑄
𝐿𝑡𝑢𝑏𝑒
𝐷𝑡𝑢𝑏𝑒
𝑄𝑠𝑢𝑝𝑝,𝑡ℎ
𝑄𝑡ℎ,𝑛𝑒𝑡
𝑄 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐
𝑇𝑟
Units
Moisture content
Heat of vaporization of water
Critical pressure
Critical temperature
Volume
Surface area
Time
Reaction rate constant
Surface reaction rate constant
Pre-exponential factor
Activation energy
Water ion product
Reaction rate
Gas constant
Reference temperature
Molar concentration of i
Volume parameter of SRK
Mathias-Copeman constants
Mass flow rate of gas outlet
Mass flow rate of dry biomass inlet
Molar flow rate of carbon in gas
outlet
Molar flow-rate of carbon in biomass
inlet
Molar flow rate of gas i
Molar yield of gas i
Log-mean temperature difference
Overall heat transfer coefficient
Convective heat transfer coefficient
of fluid i
Conductive heat transfer coefficient
of wall
Radius
Heat transfer rate
Tube length
Diameter of tube
Externally supplied heat
Net thermal energy production rate
Net specific thermal energy
production
Reduced temperature
kgwater/kgbio
MJ.kg-1
MPa
0C
m3
m2
s
s-1
m.s-1
s-1
kJ.mol-1
mol2.dm-6
mol.m-3s-1
J.mol-1K-1
K
mol.m-1
kg/hr
kg/hr
mol/hr
mol/hr
mol/hr
mol/kg
K
W.m-2K-1
W.m-2K-1
W.m-1K-1
m
MJ.s-1
m
m
MJ.s-1
MJ.s-1
MJ.kg-1
Greek
𝜀
𝛼𝑖
𝑣̃
𝜔
Dielectric constant
Stoichiometric coefficient of i
Specific volume
Accentric factor
∏
Multiplication
m3.kg-1
Abbreviations
PFR
CSTR
TOC
DP
WSHS
SCW
SCWG
SCWO
HHV
LHV
SOFC
PEM
CMC
GE
CE
CGE
Plug flow reactor
Continuous stirred tank reactor
Water soluble organic compound
Decomposition product
Water soluble humic solid
Supercritical water
Supercritical water Gasification
Supercritical water Oxidation
Higher (Gross) heating Value
Lower (Net) heating value
Solid oxide fuel cell
Proton exchange membrane
Carboxy methyl cellulose
Gasification efficiency
Carbon gas efficiency
Cold gas efficiency
MJ.kg-1
MJ.kg-1
%
%
%
1 Introduction
1.1
Background
Pursue of continual global economic growth obliges mankind to guarantee a
way to store, convert and amplify the energy. Huge difference between human
energy exploitation rate and fossil fuel recovery (millions of years) do not leave
any doubt about necessity of finding efficient and wide range applicable
renewable energy production ways. Biomass is a dispersed, energy source
contained in plants and animal wastes [1]. Energy from biomass is carbon
neutral and environmentally benign thanks to the continuous loop of carbon
between plants and atmosphere. Its exploitation is mainly done through
biological or thermal conversion processes. Biological technologies mainly use
microorganisms as energy converting agents from biomass to fuel. The oldest
thermal biomass conversion, combustion have been used by generations and in
developing countries still in general use. Biomass accounts for 14% of the world
energy consumption while even though world annual biomass potential is
estimated to be around 146 billion metric tons (almost half of the worlds annual
energy demand) [2]. Fully exploitation of biomass is not realistic and reasonable,
however it is clear that effective and widespread technologies are capable of
decreasing the consumption of other fossil fuels. Higher technology processes,
including biological technologies, have been worked in order to achieve high
energy production capacity and wide scope of biomass use. Standard gasification
has the biggest energy production share from biomass sources due to highenergy content gas product and technology maturity. Performance of the
gasification process depend many operating conditions and biomass properties,
including moisture. Thermal or electrical energy dissipated for the evaporation
of water contained in wet biomass (grass, rice straw, sugar cane, algae, manure
etc.) is still one of the biggest challenges of the current technology. However,
degradation of biomass with the presence of water is also possible according to
the early thermodynamic findings. Activity of water in the process can be
multiplied by increasing the slurry temperature near the critical point under
critical pressure (Tc, Pc). Physical changes in hot and compressed water are
favorable for rapid hydrolysis, isomerization, dehydration and some other
decomposition reactions [3]. Low carbonmonoxide (CO) compositions is a result
of favored forward water gas shift reaction (WGSR) in SCWG.
In this chapter; firstly, the biomass conversion technologies are evaluated by
their strengths and weaknesses in order to propose possible specific advantages
of the study subject SCWG. Property behavior of water in near- and superciritical
conditions is reviewed for a better understanding of the effects of water on
reaction system. Further analysis was done for the studies held on the subject in
terms of experiments, modelling studies and reactor designs.
1
1.2
Biomass Conversion technologies-Placement of Hydrothermal
Biomass Gasification
Biomass is a general term for various biological sources, byproducts and
wastes. Feasibility and capability of a process is strongly dependent on physical
and chemical properties of the feedstock supplied. The main division for biomass
processing technologies can be made as biological and thermal. The most studied
and well-known processes are classified as follows.
Thermo-chemical Processes






Combustion: Aims to produce thermal energy from combustion reaction
enthalpy
Supercritical water oxidation (SCWO)
Conventional (High Temperature) Gasification: Aims to produce syngas
(H2, CO) in oxygen deprived media
Hydrothermal Liquefaction
Hydrothermal Gasification (Supercritical Water Gasification): Aims to
produce H2 and CH4 rich product gas in hot compressed water.
Pyrolysis: Aims to produce high energy value product as solid char, liquid
biofuel and/or combustible gas.
Biochemical Processes

Fermentation

Anaerobic digestion: Aims to produce biogas as a digestion products of
bacterial growth
High temperature gasification is the most comparable technology to the
hydrothermal biomass gasification due to the aimed product similarities (H2, CO
and CH4) and scale of operation. Tradeoff between the energy recovered in the
product and energy input to run the thermochemical process mainly determines
energy efficiency and feasibility of a system. Many types of biomass, such as wet
forestry, rice straw, animal manure, algae and sewage sludge contain high
amount of moisture as received (up to 95%) [4]. Proximate analysis and calorific
values of several wet biomass types are shown in Table 1.1. Lower Heating
Value (LHV) of biomass source is estimated by subtracting the heat of
vaporization of water contained from Higher Heating Value (HHV) as shown in
𝑤𝑎𝑡𝑒𝑟
Equation 1-1, where M refers to moisture content of the fuel and ∆𝐻𝑣𝑎𝑝
means
heat of vaporization of water. Use of wet biomass for classical gas- phase
gasification and liquefaction processes therefore may result in high drying cost
and low efficiency in energetic and economical ways.
𝑤𝑎𝑡𝑒𝑟
𝐿𝐻𝑉 = 𝐻𝐻𝑉 ∙ (1 − 𝑀) + ∆𝐻𝑣𝑎𝑝
∙𝑀
Equation 1-1
2
Table 1.1 Proximate analysis and calorific values of pig manure, algae, cow manure and rice straw samples
[5]
Pig Manure
Type
Algae
Cow Manure
Rice Straw
wt%
wt%
92.10
2.80
71.87
22.61
13.88
13.67
7.01
15.64
wt%
wt%
4.05
1.05
1.67
3.85
60.52
11.94
61.95
15.40
-1.24
4.86
13.61
12.50
1.09
7.06
14.95
13.67
Proximate
Analysis
Moisture
content
Ash content
Volatile
matter
Fixed carbon
Calorific
Values
Net calorific
value (LHV)
MJ/kg
Gross
calorific
value (HHV)
MJ/kg
At this very point, hydrothermal conversion of biomass in supercritical
water is a promising alternative. Hydrothermal terminology refers to aqueous
media at high temperature and pressure close to the critical point of water (221
bar and 374 ℃). Hydrothermal gasification (or SCWG) of biomass does not
require evaporation of the water from biomass, which helps to save significant
amount of external energy depending on the moisture level of the feedstock.
High temperature and pressure water near and beyond its critical point changes
phase properties sharply from liquid to supercritical water. Biomass constituents
become miscible in hot and compressed water, resulting in lower mass transfer
resistance and higher rate homogenous phase hydrolysis reactions. Physical
changes in near- and supercritical water (in density, viscosity, number of
hydrogen bonds, dielectric constant and ion product) enables water to be not
only a solvent but also a natural catalyst and reactant [6]
Renewable combustible gas production via SCWG is advantageous at some
aspects: Firstly product gas is at high pressure (25-30 MPa), convenient for the
utilization of the stream. Next, very low CO concentration in the product gas
dismisses the need of WGS reactor. High CO2 concentration on the other hand
eases the possible CO2 sequestration. SCWG product gas composed mainly of H2
and CH4 can be mixed with the natural gas distribution network, according to the
short-medium strategy of Netherlands. In addition to that product gas can be
used for SOFC or PEM fuel cell applications, in the latter case purified H2 is
needed. Except still being a novel process, mentioned advantages create
operational options for the process in short-medium term plans (see Figure 1.1)
3
Figure 1.1 Application option scheme of SCWG process outlet [7]
Other process advantages of SCWG can be listed as follows:
[7]

Low selectivity towards char and tar forming reactions (compared to
standard thermochemical biomass conversion processes)

High solubility of CO2 in high pressure water to be separated easily in a
low pressure flash column

Low solubility of inorganics (near-critical and supercritical region) which
are separated in a solid-liquid separator

Compact process equipment due to high stream density (comparing to
standard gasification process)

Complete biomass conversion without use of catalyst (T>650℃)
Besides the exceptional advantages of the SCWG process, char and tar
plugging problems and energy intensive operating conditions are the main
drawbacks to be worked on.
4
1.3
Supercritical Water
The properties of water show variance in the wide region of temperature and
pressure from room conditions up to critical point and beyond. Critical point is
defined where temperature is critical temperature (TC=374,4 ℃) and pressure is
critical pressure (PC=22,1 MPa), for water. As depicted in Figure 1.2, the critical
point is the end point of liquid to vapor transition line in the vapor-liquid region,
along which evaporation temperature rises with pressure. Beyond the critical
point, water does not change phase simply from liquid to vapor or vice versa but
becomes single-phase supercritical fluid. In the region before and after that point
water changes its properties rapidly. For example, its dipole moment decreases
to a point of a conventional solvent, it becomes as polar as acetone and its pH
value decreases three units compared to ambient water, which results in a high
concentration of H3O+.
Figure 1.2 Phase diagram of water [8]
As can be viewed in Figure 1.3, water at its critical point acts in between
liquid water and steam, beyond the critical point the conductivity, density and
viscosity of water becomes smaller (gas like) with temperature. Specific heat
capacity of water shows a peak at which water is at critical condition. Rapid
property changes as such can be used as an advantage in an SCWG process. For
example, density change of water in limited temperature range decreases its
dielectric constant which allows the water to be a very good solvent not only for
organic compounds but also product gases which allows reactions to proceed in
one phase, resulting in higher reaction rates. The high ion product constant (KW)
of water shows catalytic effect for ionic type reactions especially in the sub- and
near-critical region where KW is the largest. SCWG of wet biomass, therefore, is a
5
challenging but attractive technology mainly due to the tunable properties of
water in the region of sub- and supercritical and its non-monotonic effects on
overall reaction kinetics [9].
Figure 1.3 Properties of supercritical water [10]
6
1.4
SCWG of biomass
The idea of combustible gas production from feed biomass with the
presence of water was initiated by the method of steam reforming of biomass to
produce hydrogen as a competitive to fossil fuel product hydrogen [11], [12].
𝐶6 𝐻10 𝑂5 + 7𝐻2 𝑂 → 6𝐶𝑂2 + 12𝐻2
The ideal stoichiometric equation was developed to interpret the reaction
of cellulose (C6H10O5) with steam for hydrogen production. In 1978 a
thermochemical equilibrium study was made by Antal [12] in order to predict the
hydrogen production from the reaction of 1 mole of cellulose with 7 moles of
steam. According to his equilibrium prediction at 600 ℃ and 1 bar no solid
carbon remained unconverted. Kinetic studies, on the other hand, showed that
steam does not take part in the fast pyrolysis of biomass at atmospheric
conditions [13],[14]. Experimental studies confirmed the kinetic studies,
resulting in char and tar formation plus the presence of high molecular weight
hydrocarbons in product gas[15]. Corella [16] reported correlated results by
observing a char yield of 10-20 % from pyrolysis of wood dust; char and tar
gasification in order to increase gas efficiency of the steam reforming at
atmospheric pressure was also investigated. It was found that tar can be to a
large extent be gasified at 800 – 875 ℃ at atmospheric pressure with calcined
dolomite as bed material in a secondary fluidized bed, but char remained partly
unconverted [16]. These findings show that the mentioned typical
thermochemical process conditions of the system are not adequate to produce
low molecular weight product gases from biomass by steam reforming at
atmospheric pressure. Biomass conversion in sub- and supercritical water (SCW)
was studied by Modell, showing tar and gas formation without char production
at pressures exceeding the critical pressure of 22,1 MPa [17]. Experiments
showed that cellulose decomposes in subcritical water rapidly, liquid water at
190 ℃ and 22,1 MPa is able to hydrothermolyse lignin and hemicellulose in a
short duration (few minutes) [17]–[19]. Solvolysis products of biomass
compounds further decompose by isomerization, condensation, fragmentation
and dehydration reactions [18], [20]–[23], producing low molecular weight gas
(composed mainly of hydrogen, carbondioxide and methane) and low amount of
tars [24].
The SCWG process, according to the studies conducted is capable of
achieving complete biomass conversion to combustible gases. Maximum process
temperature is an important parameter to point out the desired product and
decision on catalysis use. Biomass gasification in hydrothermal conditions can be
divided into three main categories [25]:



low temperature catalytic gasification (𝑇 ≪ 𝑇𝑐 )
moderate temperature catalytic or non-catalytic gasification (near critical
temperatures to 500 ℃)
high temperature catalytic or non-catalytic gasification (𝑇 > 500 ℃)
7
In this work our scope of investigation is high temperature non-catalytic
gasification at 500 – 800 ℃ to produce combustible gases, which will be
expressed simply as SCWG.
8
1.4.1 Main Research Groups and Experiments on SCWG
There are five main research groups that have conducted different experiments on SCWG of
biomass.
Table 1.2 displays the operating conditions, feedstock and reactor systems of the
researches [25].
Table 1.2 Selected SCWG research groups [25]
T( ℃)
Research
Feedstocks
Group
Antal, U. of <22 wt.% wet biomass (corn
~600
Hawaii
starch, potato starch and
wastes, wood sawdust, water
hyacinth, cellulose, macadamia
nut shell, sugar cane bagasse,
glucose and other model
compounds)
P (MPa)
Reactor System
Ref.
22-34.5
Hastelloy
&
Inconel
tubular reactors, capillary
tube reactors, packed bed
systems;
catalysts:
activated
carbon
and
charcoal
[26]–
[30]
Kruse,
Dinjus and
Boukis,
FZK,
Karlsruhe
1–5 wt.% glucose, vanillin, 400–700 25–50
glycine,
sawdust,
straw,
cellulose, plants, meats, corn
silage with ethanol and
pyroligneous
acid,
pyrocatechol and phytomass,
corn starch, clover grass,
sewage sludge and lignin
Pilot plant, 2–4 min; batch,
CSTR, tubular reactors; 60
s–1 h; alkali catalysts:
KOH, K2CO3 and KHCO3
[31]–[33]
van Swaaij,
U.
of
Twente,
Netherland
s
Matsumura
, U. of
Hiroshima,
Japan
Model compounds of 1–18 460–800 24–30
wt.% formic acid, glucose,
glycerol and pinewood
[34]–
[36]
State Key
Lab (Xi’an
Jiaotong
University,
China)
Sawdust, rice straw, rice shell, 600–800 25
wheat stalk, peanut shell, corn
stalk, corn cob, sorghum stalk,
CMC (carboxymethylcellulose),
cellulose and glucose
10–90 s, novel screening
technique using fusedquartz capillary tubes (id
¼ 1 mm); catalysts: alkali
metal and Ru/TiO2
Pilot plant, hydrothermal
pretreatment,
partial
oxidation (H2O2), batch
SS316 tubes, <20 min,
fluidized bed reactors;
catalysts: alkali metal,
nickel and metal oxides
Miniature plant and bench
scale tubular reactors
Sawdust, rice straw, cabbage; <600
model compounds: cellulose,
xylan, lignin reagents and
glucose
25
9
[37]–
[39]
[9],
[40],
[41]
Research group of Antal and co-workers from University of Hawaii have
investigated the wide range of biomass gasification in hydrothermal media in
1990. According to the experiments, promising conversion results as high as
>85% and also suppression of tar and char forming reactions were obtained.
Catalytic effects of metallic reactor and material and the use of activated carbon
for catalyst are other topics investigated by this group [26]–[30].
Kruse and Dinjus at the Institut für Technische Chemie , FZK in Germany
worked on wide range of biomass types by using batch and continuous tubular
and mixed reactors. The starting investigation of the study points out the process
conditions that maximize the hydrogen yield in product gas. Complete biomass
conversion to a high hydrogen yield product gas was experimented with addition
of K2CO3 and KOH alkali salts at supercritical conditions (600 ℃ and 25 MPa).
Researchers have constructed a SCWG pilot plant “ Verena” in 2003, with a flow
capacity of 100 kg h-1 and maximum operating temperature and pressure of 660
oC and 35 MPa, respectively [31]–[33].
The chemical engineering research group of University of Twente,
Netherlands have contributed to the subject by introducing quartz capillary flow
tubular reactors that can work at well beyond critical temperature of water (600
℃). Use of a quartz reactor allowed the group to clearly identify the reaction
kinetics with/without catalytic effect since it is known that metal reactor surface
is prone to catalytic activity at high temperature and pressure. SCWG of glycerol,
glucose and pinewood was investigated in a quartz capillary reactor, it was found
that non-catalytic complete gasification of biomass is possible but for very low
feed loads [34]–[36].
Matsumura has been investigating the SCWG of biomass at Hiroshima
University in Japan with the collaboration of various researchers. His group’s
main interests are the pretreatment of biomass to enable high biomass load feed,
and lower temperature catalytic biomass conversion in hydrothermal conditions.
Kato and Matsumura achieved a high concentration cabbage slurry feed by feed
pretreatment processing taking place between 147–197 ℃ .The researchers have
constructed a power plant which is located in the university campus of
Hiroshima [37]–[39].
The research group of State Key Lab, China has been conducting studies on
gasification of biomass feedstock in hydrothermal media since 1977. Their
research covers a wide scope of biomass, including glucose, cellulose, sawdust,
rice straw, rice shell, wheat stalk, peanut shell, corn stalk, corn cob, and sorghum
stalk. Experiments run at or close to 25 MPa and 650 ℃ resulted in high biomass
gasification efficiencies (> 85%) as well as high hydrogen and low CO yield [9],
[40], [41].
Process simulation of the SCWG of biomass has been conducted in literature
with chemical equilibrium considerations for the reacting system. Even though it
is a hypothetical assumption and provides output data without time and
dimension inside the reactor, such modeling work is vital for analyzing
thermodynamics of the system.
Fiori et al. [42] simulated the SCWG of biomass process for various feedstock,
such as glycerol, microalgae, sewage sludge, grape marc and phenol. An
AspenPlusTM process flow sheet was built in order to analyze the product gas
10
composition and yield at different operating conditions as well as to determine
the thermal efficiency and feasibility of the process. An SCWG reactor was
simulated using the ‘RGibbs’ module of the software, which applies Gibbs free
energy minimization to quantify product gas composition. Process units of the
study were feed pump, heat exchanger, Gibbs reactor, gas-liquid separator and
gas burner. It was found, according to the simulation, that thermal sustainability
of the process can be achieved if 15-25% of feed concentration is applied. The
maximum hydrogen yield was found as 8,5 kg H2/100 kgfeed. The net power
achieved for 1000 kgfeed/hr was 150 kWe, by utilizing hydrogen in the fuel cell
and expanding gas product of the reactor.
11
1.5
Reactor Systems
Biomass gasification under hydrothermal conditions have been studied since
1970’s. It should be noted that except continual efforts in researches and
findings it is still defined as a novel process [7]. Optimal reactor type and
reaction parameters have been investigated which will favor the gas production
efficiency, overcome reactor plugging and shut down problems and enable high
throughput. The largest plant in operation was built in 2003 in Germany (FZK) in
order to process wet wine distillery residues with a design capacity of 100 L h-1
[25]. European subsidies and grant awarded by Japanese NEDO supported the
construction of a process development unit (PDU) in Enschede, Netherlands with
a design capacity of 30 L h-1, which is devoted to the Ph.D. research studies held
in University of Twente. There are other smaller scale units for laboratory work
of University of Hawaii, Osaka Gas, Pacific Northwest National Laboratory,
Hiroshima University and TNO and BTG (in Netherlands) [7].
Batch Reactor Applications
Batch autoclave reactors are used for analyzing product yield and
composition, with simpler operation at supercritical temperature and pressure
conditions. Main drawbacks of batch autoclaves can be stated as high heat
transfer resistance (i.e. long heat up period), resulting difficulties in
understanding kinetic response of the system to the temperature and
uncontrollable catalytic effect due to the reactor material [7].
University of Twente has been investigating batch operations in quartz
capillary reactor which shows great advantage in operation simplicity and
observation abilities but difficulties in pressure measurements [7].
Continuous Reactor Applications
Continuous flow reactors are of great importance in order to scale up the
technology to the commercial level. Continuous stirred tank reactors (CSTR’ s)
are less favored than the tubular reactors owing to mechanical challenges in
mixing ability and heat insulation as well as low concentration feed requirement
due to the rapid dilution to the effluent concentration (obliges bigger reactor
volume than tubular reactor for the same throughput). Tubular reactors on the
other hand may suffer from plugging issues due to the char and tar deposition
[7], [43]
Fluidized bed reactors are capable of solving char and tar related plugging
issues and also high heat and mass transfer capabilities. Nature of SCWG requires
more work for fluidized bed technology; currently research group in University
of Twente conducted investigation on micro fluidized bed reactor as a part of
NEDO Grant Research[7].
A PDU tubular reactor system was built for SCWG of biomass operation,
which comprised of a continuous flow reactor, which is the most studied reactor
system with straightforward operation and control. Figure 1.4 shows the general
process scheme of the PDU. There are four liquid containers for continuous feed
12
sufficient for at least 2 hours of operation. A liquid feed pump pressurizes the
stream and pumps it into a heat exchanger, where heat is exchanged between
outlet and inlet streams. Efficient heat exchange between the fluids is vital for
feasibility of the process; in this example the cold stream flows through the outer
tube and the hot stream through the inner tube. Near-critical fluid is fed to the
tubular reactor, which operates at 650 ℃ and 300 bar, and is heated up
externally by natural gas burners. Product gas is separated from water in high
and low pressure flash separators. High solubility of CO2 in supercritical water is
advantageous for separation operation simplicity.
Figure 1.4 Simplified process scheme of the PDU for SCWG of biomass operated at University of Twente [7]
It is noteworthy that, for a successful reactor design with high gasification
and thermal efficiency and low char/tar formation, reaction kinetic data of
hydrothermal gasification of biomass (in the whole range of operation) needs to
be collected; by which favored operation conditions can be realized for desired
reaction pathway and products.
13
1.6
Scope and Objectives of the Study
Biochemical compounds in biomass undergo various decomposition
reactions in hydrothermal media. There have been several experimental and
thermodynamic modeling works on SCWG ranging between simple model
compounds to real biomass samples. It has been found that SCWG is a promising
technology for producing high calorific value gas from wet biomass, with less tar
and char formation owing to the high solubility of solid biomass in hot and
compressed water and rapid hydrolysis reactions [7]. Furthermore, catalytic
effects of water involving ionic mechanisms have been reported especially in
near-critical region at which water ion product boosts.
For product distribution, design and optimization of a reactor, kinetic
approach to the process is necessary by which rate laws and kinetic parameters
of the biomass decomposition pathway can be quantified. There are few kinetic
studies for SCWG [44]–[47], mostly focused on ultimate results such as
gasification efficiency and gas yields. Reaction paths including the intermediates
(source of gas products) and direct pyrolytic gasification reactions have not been
studied together; hence, limited knowledge is present on rates of reactions
constituting the biomass degradation reaction network. Kinetic and process data
in the literature are available for specific model compounds and for low flowrates due to the time and space limitations of the experimental set-ups. Process
realization, on the other hand, requires the analysis of real biomass slurries with
higher feed flow-rate and in a broad range of operating parameters, combined
with possible variations in the reactor design specifics.
Computer aided simulation is an efficient tool for multipurpose
evaluation of the process parameters and design specifications (e.g. operating
pressure and temperature, residence time, biomass type, feed composition etc.).
Kinetic approach and simulation require careful review and collection of the
biomass hydrothermal decomposition data in the whole relevant operation
range, enabling the instantaneous composition analysis inside the reactor.
In this set of work, a reaction kinetics approach to the “SCWG of biomass”
process was applied, with the objective of predicting distribution of key
compounds from the initial heating up of the feed stream till the end product.
Main goal of the work is to elucidate the main decomposition and gasification
reaction pathways of complete biomass hydrocarbons and to analyze the
influence of operational condition changes on gasification and thermal efficiency
of the process. Possible solutions for the ongoing process challenges are aimed at
by analyzing the instantaneous kinetic response of the reacting system to the
tuned variables.
14
Research questions to be answered:

Are the reported experimental kinetic data for individual model biomass
compounds capable of predicting the real lignocellulosic biomass (mainly
agricultural residues) behavior?

Is AspenPlusTM software capable of simulating the SCWG of model
compound combinations and deliver valid results based on the reaction
kinetics approach?

What can be the possible process conditions to favor high gas yields and
low tar/char formation yields?

Is biomass gasification based on the SCWG process feasible with
reasonable yields and thermal self-sufficiency?
15
2 Literature Review of Biomass Degradation in
Hydrothermal Media: Reaction Mechanism
and Kinetics
Early studies were performed by Antal, on standard gasification of
lignocellulosic biomass. A detailed reaction kinetics pathway approach to the dry
gasification of biomass is challenging, due to the complexity of the reacting
conditions[4]. Dry gasification of biomass takes place heterogeneously and
mostly surface enhanced, adding to that it has complex structure consisting of
cellulose, hemicellulose and lignin [4]. Nature of the classical gasification
prevents to define the degradation of biomass reaction pathway with precise
single reaction steps.
Biomass conversion in hot and compressed water is based on a
significantly different reaction mechanism than air blown gasification, by
enabling water to be a reaction medium and a solvent. Crystal cellulose (initially)
degrades to sugar rapidly in hot and compressed water via a hydrolysis pathway
instead of pyrolysis[4]. Hydrolysis products generally show high solubility in
hot-compressed water, makes the medium a homogenous, one phase solution.
Biomass gasification in sub- and supercritical water, from solid biomass to the
product contains very high number of grifted reactions. Pathways through key
decomposition compounds would enable the kinetic modeling of the complex
reaction network. Main intermediate compounds of the biomass conversion in
sub- and supercritical condition are aldehydes, ketones, furfurals and organic
acids[4].
In this chapter, kinetic studies and reaction pathway proposals of various
science groups are reviewed based on the three main biopolymer compounds.
Decomposition reactions and mechanisms, main products, compositional data
and proposed reaction networks for cellulose, hemicellulose and lignin are
described in Chapters 2.1, 2.2, 2.3, respectively.
16
2.1
Cellulose Decomposition
Cellulose is the most abundant constituent of the lignocellulosic biomass
sources. Initially, cellulose decomposition with acid catalyst or enzymes was
studied and hydrolysis to simple saccharides namely glucose and fructose was
achieved[48][49]. The use of catalysts is found useful for increasing the solubility
and reactivity of cellulose by breaking the inter-intramolecular hydrogen bonds
[50].
Hot and pressurized water changes its physical properties significantly,
namely ionic product, density and viscosity which enables the cellulose
decomposition reaction to take place without a catalyst. Cellulose (and glucose)
decomposition in hydrothermal media follows several organic reaction
pathways, such as hydrolysis, retro-aldol condensation, keto - anol tautomerism
and dehydration [22], [23], [51], [52].
Kabyemela et al. studied the reaction kinetics and primary products of
cellobiose decomposition. A biomass-water mixture with a 16 g/min flow rate
was heated up and pressurized rapidly to the near- and supercritical condition
(25-30 MPa, 300 – 400 ℃ and fed to a stainless steel reactor. Residence time was
kept as low as 0,04 to 2s in order to analyze the primary products of cellobiose
decomposition.
Product concentrations obtained revealed that cellobiose conversion
reaches 80% in 1,4 s at 350 ℃; and the same degree of conversion is achieved in
0,8 s at 400 ℃ . Glycosylerythrose (GE) and glycosylglycolaldehyde (GG)
compositions are nearly constant; glucose and fructose compositions reach a
maximum before converting to other product. Erythrose composition reaches
the maximum with residence time. Cellobiose undergoes conversion by
hydrolysis of glycosic bond and pyrolysis of the reducing end resulting in GE+GG
and glucose + fructose, respectively [51]. Kabyemela proposes a reaction
network for cellobiose decomposition thanks to the product distribution (see
Figure 2.1). Notation kH refers to the hydrolysis pathway, k1 and k2 stand for
pyrolysis reactions.
Figure 2.1 Cellobiose decomposition pathway: 𝟑𝟎𝟎 ℃ < 𝑻 < 𝟒𝟎𝟎 ℃, 𝟐𝟓 < 𝑷 < 𝟒𝟎 𝑴𝑷𝒂 [51]
17
The linear relation of –ln(1-X) versus residence time, with X being the
cellobiose conversion, shows that reaction behavior follows a first order
decomposition reaction rate (see Figure 2.2).
Figure 2.2 -ln(1 - X) versus residence time for cellobiose decomposition [51]
The reaction type of cellobiose depolymerization changes from
heterogeneous to homogenous when near critical conditions are reached via
dissolution of cellulose in water [50]. Figure 2.3 shows the temperature relation
of the overall cellulose decomposition rate constant in between 290 and 400 ℃
and 25 MPa.
18
Figure 2.3 Arrhenius plot of the rate constant of conversion of microcrystalline cellulose in water (290 C –
400 oC, 25 MPa), [50].
A surface enhanced reaction model (see Equation 2-1) was proposed by
Sasaki et al. in order to explain the reactivity of microcrystalline cellulose in suband supercritical water[50].
𝑑𝑉(𝑋)
= −𝑘𝑠 . 𝑆(𝑋)
𝑑𝑡
Equation 2-1
where 𝑘𝑠 is surface reaction-rate constant (m.s-1), 𝑉 and 𝑆 are volume
(m3) and surface area (m2) of the particle, respectively.
Reaction rate calculations showed that conversion of cellulose in
supercritical water is faster than in sub-critical water. The main reason of the
change in kinetic parameters can be given as, from sub- to supercritical water
surface driven reaction switches to homogenous reaction due to the enhanced
swelling and dissolution of biomass. Hydrolysis is the favored mechanism in subcritical water while in supercritical water radical pyrolytic decomposition
reaction is favored with high temperature and the mixture being homogenous.
Kinetic parameters of overall cellobiose decomposition are Ea=145,9 and
A=1011,9 s-1; Ea= 547,9 kJ.mol-1 and A=1044,6 in sub- and supercritical water,
respectively.
19
2.1.1 Glucose Decomposition
Early glucose decomposition kinetic studies in hot compressed water
show that, glucose isomerization to fructose, glucose decomposition and fructose
decomposition are the main reactions. Glucose epimerizes to the fructose,
dehydrates to the anhydroglucose or decomposes to the intermediates by
breaking C-C bonds under hydrothermal conditions. 1,6 anhydroglucose,
dihydroxyacetone, pyruvaldehyde and small amount of 5-HMF are the
decomposition products, detected in the liquid effluent [23]
Furthermore, kinetic studies have been conducted by researches on
glucose decomposition in sub- and supercritical water in order to obtain a
complete reaction scheme. According to the Kabyemala et al., glucose and
fructose reacts to form several organic compounds such as aldehydes, ketones
and organic acids in hydrothermal conditions (573-673K and 25-40 Mpa) [53].
Glucose also isomerizes with fructose. Decomposition occurs mainly via Lobry de
Bruyn Alberda van Ekenstein (LBAE) transformation, retro-aldol condensation,
bond cleavage by double bond rule and dehydration reactions [53]. In Figure 2.4
it is shown that glucose ring structure opens up and forms intermediate
aldehyde, ketone or enediol by LBAE transformation. Retro-aldol condensation
products of glucose are glyceraldehyde and erythrose. Bond cleavage with
double bond rule forms glycolaldehyde and also glyceraldehyde. Glucose also
forms anhydroglucose by dehydration reaction. Glyceraldehyde undergoes
reversible isomerization reaction with dihydroxyacetone, both of which
dehydrates to form pyruvaldehyde [22]. The following step for the intermediate
compounds formed is the hydrolysis to the organic acids [54].
20
Figure 2.4 Mechanism of glucose decomposition in sub- and supercritical water: 𝟑𝟎𝟎 ℃ < 𝑻 < 𝟒𝟎𝟎 ℃, <
𝑷 < 𝟑𝟎 𝑴𝑷𝒂 [53]
With the knowledge of main liquid products of hydrothermal glucose
decomposition, possible char, furfural and 5-HMF formation reactions were
investigated by Promdej and Matsumura, in between 300 ℃ – 500 ℃ and 25 MPa
[3]. According to the lumped approach to the reaction network, main
decomposition products of glucose and fructose were classified as 5-HMV,
furfural, char, gas and the rest of water-soluble organic compounds (TOC). The
effect of temperature on glucose conversion in sub- and supercritical water was
analyzed with the aim of finding out not only the reaction network but also the
mechanisms. Tar and char formation, among the main challenges of SCWG of
biomass technology can be suppressed by tuning the water properties with the
knowledge of compound favoring conditions. It was found that there are both
ionic and radical reactions taking place during the glucose and fructose
decomposition. Ionic type reactions were identified according to the nonArrhenius behavior in supercritical water due to the rapid decrease in density
and ion product of water. Water below the supercritical region more efficiently
dissociates into H3O+ and OH- ions, resulting in ion product increase 103-4 orders
of magnitude (compared with normalized condition). Ionic reaction rates are
favored by (increasing) temperature until the supercritical region, beyond which
temperature has inverse effect on rate due to decrease in density (gas like
21
behavior). For radical reactions, obedience to the Arrhenius behavior is observed
in sub- and supercritical water. Promdej and Matsumura suggested an ionic
mechanism for 5-HMF, furfural and char formation and also fructose to TOC
decomposition; on the other hand radical mechanism reactions pointed out were
glucose, 5-HMF and furfural to TOC decomposition reactions in addition to the
radical gasification reactions [3]. Char formation from glucose decomposition
was also linked to the ionic reactions, unlike proposed radical reactions of lignin
to char, which is discussed specifically in Chapter 2.3.
According to their set of experiments, Promdej and Matsumura suggested
order reaction for all glucose decomposition pathway reactions. Figure 2.5
displays the pathway of glucose conversion in hydrothermal conditions.
1st
Figure 2.5 Glucose decomposition pathway in sub- and supercritical water: 𝟑𝟎𝟎 ℃ < 𝑻 < 𝟒𝟔𝟎 ℃, 𝑷 =
𝟐𝟓 𝑴𝑷𝒂 [3].
22
2.2
Hemicellulose Decomposition
Being one of the key components of the biochemical structure of biomass,
hemicellulose and its degradation products in sub- and supercritical conditions
is of great importance. D-Xylose, 5-carbon sugar is a main hydrolysis product of
hemicellulose. D-xylose, as a monomer unit of hemicellulose has been
investigated widely in order to understand the reaction kinetics of hemicellulose
in sub- and supercritical water. According to the Sasaki’ s experiments with a
flow type reactor, D-xylose decomposes via reverse aldol condensation in suband supercritical water (633 K-693 K, 25-40 MPa) [55].
Kinetics of xylose and its primary decomposition product furfural under
hydrothermal conditions were investigated by Qi et al.[56] for temperatures
between 180 ℃ and 220 ℃ and pressures below 10 MPa. Product composition
distribution was reported in order to elucidate the decomposition network. It
was found that xylose primarily undergoes decomposition to furfural (k1) and
hydrolysis to undefined decomposition products, DP1 (k2). Furfural reacts in
hydrothermal media to produce other products, DP2 (k3) (see Figure 2.6).
Figure 2.6 D-xylose decomposition in hot compressed water: 𝟏𝟖𝟎℃ < 𝑻 < 𝟐𝟐𝟎℃, 𝑷 = 𝟏𝟎 𝑴𝑷𝒂 [56]
For the overall reaction network elucidation, definition of DP1 and DP2
were required which would enable the modeling of the complete system. In
literature, decomposition product definitions were not found for xylose in
subcritical water. It was accepted that in supercritical water xylose and furfural
decompose to form similar products but with possible different rate parameters.
Goodwin et al. [57] studied the kinetic model of xylose decomposition for both
analysis of intermediate DP’ s and simplified gasification pathway, for
temperatures between 450 ℃ and 650 ℃ under 250 bars. According to the
kinetic study of concern, xylose decomposition takes place in two directions; to
furfural and to glyceraldehyde, methyl formate mixture. Furfural may react in
two ways, to produce either water soluble humic solution (WSHS) or to maple
lactone (C6H8O2), as shown in Figure 2.7. In other kinetic study of Goodwin and
colleagues, WSHS as a decomposition product of furfural was analyzed to be a
mixture of acetic acid and acrylic acid [58].
23
Figure 2.7 Kinetic model reaction mechanism for decomposition of xylose in supercritical water 𝟒𝟓𝟎℃ <
𝑻 < 𝟔𝟓𝟎℃, 𝑷 = 𝟐𝟓 𝑴𝑷𝒂 [57]
According to the kinetic study of the Goodwin, for high temperatures (450
℃- 650 ℃) radical gasification reactions are favored and more precise product
compositions were achieved with a simplified gasification pathway due to the .
According to the proposed network, xylose either dehydrates to furfural or
decomposes to WSHS. Furfural may undergo further decomposition to WSHS,
which is the intermediate between liquid organics and gasification products, CO
and H2 (see Figure 2.8).
Figure 2.8 D-xylose decomposition and gasification path in supercritical water: Kinetic model reaction
mechanism for decomposition of xylose in supercritical water 𝟒𝟓𝟎℃ < 𝑻 < 𝟔𝟓𝟎℃, 𝑷 = 𝟐𝟓 𝑴𝑷𝒂 [57]
24
2.3
Lignin Decomposition
Lignin, one of the most abundant biochemical units of biomass with
polymeric aromatic organic substance with oxygen-based functional substituents
(such as hydroxyl and methyl groups), has been investigated for its kinetic
behavior in sub- and supercritical water. Kanetake et al. [59] used guaiacol as a
model compound of lignin, owing to its similar polymeric structure and
functional units. Kinetic studies were performed for temperatures between 380
℃ and 400 ℃ under various pressures for long residence times up to 90 minutes.
GC-MS chromatogram peaks of the liquid effluent revealed that main
decomposition products of lignin are catechol, phenol and o-cresol; additionally,
some gaseous products were detected but not quantified (see Figure 2.9)
Figure 2.9 GC-MS chromatogram for soluble products of guaiacol decomposition in supercritical water [59]
Kinetic modeling was carried out by accepting 1st order decomposition
reactions even though product compositions did not follow precisely, which
were reasoned with direct gasification of guaiacol under supercritical conditions
[49]. A reaction mechanism was suggested that dissolved compounds containing
ether linkages form single ring phenolic compounds (catechol, phenol and ocresol) which may further re-polymerize to form high molecular weight
compounds. Char residues after the supercritical water treatment were analyzed
was and these were explained to be the dehydration mechanism product of low25
molecular phenolic compounds, monomers and oligomers [49]. The resulting
proposed reaction mechanism of Kanetake and colleagues is displayed in Figure
2.10.
Figure 2.10 Simple reaction pathway for guaiacol (lignin model component) in supercritical water [59]
Catechol reaction kinetics and mechanism in sub- and supercritical water
was further investigated by Wahyudiono et al. [60] for temperatures between
370 ℃ to 420 ℃ and residence times up to 240 minutes. Decomposition reaction
rate was found to be extremely slow due to the strongly bound heteroatoms and
carbon atoms even in the presence of supercritical water. Phenol formation from
catechol mechanism was explained as follows; initiation of the reaction by
scission of one of the O-H bonds and formation of phenoxy radical, further attack
of the released H to the radical, resulting in phenol and OH- ion (see Figure 2.11).
26
Figure 2.11 Simple reaction pathway of catechol decomposition in near- and supercritical water [60]
Yong et al. [61] investigated the reaction network and kinetic parameters
of the guaiacol depolymerization in sub- and supercritical water for between 300
and 450 ℃ at a pressure of 25 MPa and for a very short residence time in
between 0.5 – 40 s. According to the analysis of the flow reactor outlet stream
the main decomposition products were found to be the single ring phenolic
(phenol, catechol, cresol) and benzene structured aromatics in addition to gas
and char[61]. Kinetic parameter calculations showed that overall guaiacol
decomposition obeys the Arrhenius law, while in the network deviations from
Arrhenius law was observed for some reactions (especially under supercritical
conditions), which suggested the presence of different mechanisms in the
network[61]. According to the yield analysis, formation reactions of some
guaiacol degradation compounds were favored in subcritical condition while the
rest was rather dominant in the supercritical water [61]. Water properties, such
as dielectric constant (𝜀) and ion product (𝐾𝑤 ) deviate significantly from the
ambient water when sub and near-critical conditions are achieved; 𝜀 decrease (≈
10) and 𝐾𝑤 increase (≈10-10) favor ionic reactions and result in high rate
constants. Beyond critical point at elevated temperatures, suppression of those
mechanisms due to gas phase behavior of water can be observed. In their set of
experiments, Yong et al. observed that guaiacol decomposition increases with
temperature, especially in the supercritical region and it goes to near complete
conversion within 40 s, indicating the radical type depolymerization of guaiacol,
by chemical degradation of the aromatic ring substituent[61]. Gaseous product
detection for short residence times especially in near- and supercritical
conditions suggested the direct gasification of guaiacol and liquid products via
radical reactions[61].
27
Figure 2.12 Catechol formation from guaiacol in sub- and supercritical water [61]
Complex reaction mechanisms of the phenolic and benzene products
were studied [61], according to which catechol was found to be formed from
guaiacol via both radical and ionic mechanisms as shown in Figure 2.12. Benzene
formation reaction from guaiacol was dominant in the near-critical region, while
it was not formed under supercritical conditions, pointing out the likely
mechanism to be ionic. The high yield of phenol in the subcritical region
indicated the presence of ionic reactions even though mechanism was found to
be through the formation of phenolic radicals (see Figure 2.13). Hence, further
kinetic parameters for the aromatic compound reactions were quantified but for
some reactions (including the formation of phenolic compounds) mechanism
propositions were left for future studies [61].
28
Figure 2.13 Phenol formation from guaiacol and catechol in sub- and supercritical water [61]
Formation of compounds composed of multiple benzene rings,
abundantly found in the supercritical region was a fruitful information for
understanding the indication of cross-linking between the single ring compounds
[61]. Especially in the supercritical water high molecular weight fragments were
formed as a result of the cross-linking reactions between the degradation
fragments (radicals) [61], which are prone to combine further to produce char
particles.
An overall reaction pathway was elucidated from guaiacol to the aromatic
degradation products, inter-conversion of them and formation of gas and char
products[61]. The complex reaction network resulting (see Figure 2.14) suggests
that guaiacol depolymerizes to catechol, cresol, phenol and benzene; catechol is
only formed from guaiacol and converts to cresol and phenol; cresol is a
hydrothermal degradation product of guaiacol and catechol, which may further
convert to TOC; benzene is produced from guaiacol and TOC. In addition to that,
char formation is sourced by guaiacol, phenol, TOC and benzene while gaseous
products are formed from either guaiacol or TOC.
29
Figure 2.14 Reaction pathway of guaiacol under hydrothermal conditions: 𝟑𝟎𝟎℃ < 𝑻 < 𝟒𝟓𝟎℃, 𝑷 =
𝟐𝟓 𝑴𝑷𝒂 [61]
Benzene and phenol are the essential decomposition products of lignin
under hydrothermal conditions. Reaction network and kinetics of the benzene
and phenol decomposition in SCW was investigated in the kinetic work of Yong
et al. [39] for temperatures between 370 – 450 ℃ under 250 bar pressure within
a short residence time between 0.5-100 s. According to the proposed network,
gas production was sourced by phenol, benzene and TOC and char is formed by
phenol, benzene and naphthalene polymerization reactions (see Figure 2.15).
Figure 2.15 Reaction scheme of phenol benzene decomposition in supercritical water: 𝟑𝟎𝟎℃ < 𝑻 <
𝟒𝟓𝟎℃, 𝑷 = 𝟐𝟓 𝑴𝑷𝒂 [39]
30
3 Proposed Reaction Pathway
Kinetics of the hydrothermal decomposition of biomass constituents has
been studied based on experiments by several research groups as mentioned in
Chapter 2. Most of these works; on the other hand, were targeting to clarify
kinetics of a specific constituent (cellulose, hemicellulose or lignin) in a limited
temperature range; according to their own scientific interest and missing works
in the literature. For the application of the SCW gasification process simulation,
with real biomass (composed of cellulose, hemicellulose and lignin) feed,
integration of the most reasonable pathways with available kinetic parameters
was the scope of the current work.
By proposing an integrated decomposition pathway and deriving kinetic
parameters from published reaction rates, this set of work enables the
compositional analysis of the degradation products of a biomass with the
flexibility of different feedstock and thermal condition. Integration of the kinetic
works was established by choosing the sequential study of the same research
groups for given model compounds, if available, otherwise pathways were linked
only through the end products in order to preserve the liability of the kinetics
data achieved according to the compositional distribution over a set network.
Additionally, experimental conditions under which the data gathered were
worked carefully by grouping the data according to the severity of the operating
conditions. For reaction pathways, reported to show difference between sub –
and supercritical water, grouping was made in order to identify better the
decomposition products in specified operating conditions. In addition to that
kinetic parameters of several reactions, with deviations from Arrhenius
parameters in supercritical water were handled separately by considering the
possibility of application to the simulation and extent of influence on the outlet
compositions. Rapid decomposition reactions of the inlet compounds, reported
to achieve complete conversion in a matter of seconds in subcritical water during
the average residence time spent for the heating up the stream. On the other
hand, for rather slowly reacting compounds, such as lignin derivatives, separate
pathways were proposed for sub- and supercritical water in order to minimize
the errors due to the influence of reaction mechanism. In spite of all, content of
the study done, relies on the reported kinetic findings, which in some cases
obliged to work with assumptions; details and reasoning of which are given in
the following sections.
Separate studies were carried out for three of the most abundant
biochemical compounds in lignocellulosic biomass, cellulose, hemicellulose and
lignin. Model compounds were chosen as cellobiose, xylose and guaiacol for
cellulose, hemicellulose and lignin, respectively; all of which are the main
decomposition products of the associated biopolymers with similar chemical
structure.
31
3.1
Cellulose Pathway:
3.1.1 Decomposition of Cellobiose
In the proposed reaction network, cellobiose as a model compound of
cellulose undergoes to series and parallel reactions to form simpler organic
compounds in hot and compressed water. Cellobiose initially depolymerizes in
three essential ways; pyrolysis to glucosyl-erythrose + glycolaldehyde, glucosylglycolaldehyde + erythrose and hydrolysis to the glucose [51].
Glucose isomerizes to the fructose but fructose to glucose isomerization is
not significant. Glucose under hydrothermal conditions, decomposes to
erythrose, glyceraldehyde, 5-HMF and also dehydrates to 1,6 anhydroglucose.
Fructose decomposition reactions produce glyceraldehyde, 5-HMF and furfural.
Dihydroxyacetone takes place in 2-way isomerization reaction with
glyceraldehyde. Pyruvaldehyde is a dehydration product of the isomer
compounds. Even though the char formation from 5-HMF polymerization was
observed in the subcritical region [3], kinetic data reported was not sufficient for
the use (less than three rates were reported for different temperatures) in the
model, hence excluded. The proposed pathway (see Figure 3.1) was mainly
derived from the kinetic work performed for temperatures between 300 and 400
℃, which may result in differences between the actual case and simulation for
elevated temperatures, although simulations showed that cellulose to organic
acids or to aromatics (i.e. furfural, 5-hmf) is completed rapidly, time required for
complete conversion is shorter than the time spent in the subcritical region
during the heat up period in the subcritical reactor [9], [23].
32
Figure 3.1 Reaction scheme of cellobiose in near critical water: k1, k2, k3, kge.g, kgg.g from [51], kg.e, kg.a, kg.gly,
kg.f, kg.a, kf.e, kf.gly from [53], kgly.dih, kdih.gly, kgly.p, kdih.p from [22], kg.5, kf.5, kf.fu , kfu.ch from [3]
3.1.2
Reactions of Glucose-Fructose decomposition products
The proposed pathway of intermediates formed from cellobiose
decomposition is mainly directed towards formation of organic acids.
Decomposition from fructose, aldehydes, ketones to the organic acids was
reported in the literature [53], [54], without indication of the reaction equations.
Organic acid formation assumption was made according to the number of carbon
atoms (that would result in 1st order decomposition reactions) and possible acid
types detected in the SCWG effluent. Glycolaldehyde to acetic acid reaction was
added to the network with the assumption of similar mechanism with the other
aldehyde types in order to prevent substance’s accumulation in the system.
Rate constants gathered for the intermediate decomposition reactions are
in the same range of glucose decomposition reactions, which results in the same
assumptions and error margins, except for 5-HMF decomposition. The complete
reaction network is displayed in Figure 3.2
33
Figure 3.2 Intermediate decomposition scheme kf.acid, kp.acid, ka.acid, ke.acid [54]; k5.lf, k5.ff [62]
34
3.2
Hemicellulose Pathway
D-Xylose was assumed to be the model compound, which is one of the
main decomposition product of hemicellulose (e.g. arabinose, glucose) and
widely studied compound for hydrothermal processes. D-xylose decomposition
reaction pathway is displayed in Figure 3.3 and Figure 3.4 for sub – and
supercritical region, respectively.
The subcritical reaction pathway shows that D-xylose primarily
decomposes to glyceraldehyde and methyl formate or forms furfural. Furfural
further decomposes to the mixture of acetic acid and acrylic acid [56]. The Dxylose subcritical water decomposition reaction network is obtained from the
research performed in the rather mild temperature and pressure condition (180
C- 220 oC, 10 MPa)[56]. For conditions near-critical region extrapolation of the
kinetic data according to the Arrhenius law may result in errors. The extent of
deviation due to the data collected is commented in paragraph 4.3.
Figure 3.3 Decomposition scheme of D-xylose in subcritical water [56]
In the supercritical water, D-xylose decomposes to the furfural or to an
acetic acid and acrylic acid mixture; furfural undergoes a parallel reaction to the
products of char (polymerization) and acetic acid-acrylic acid solution (WSHS).
Acetic acid-acrylic acid mixture gasifies to CO and H2 beyond the supercritical
point of water [57] Figure 3.4 shows the proposed pathway.
35
Figure 3.4 D-xylose decomposition and gasification scheme in supercritical water kxy.fu,kxy.wshs,kfu.wshs,kaa.ga
from [57]
36
3.3
Lignin Pathway:
Guaiacol was selected as model compound for the kinetic investigations of
lignin due to its chemical structure and similar attached groups [61]. Guaiacol
decomposition was studied separately in sub- and supercritical water since there
exists sufficient literature data for detailed analysis in both regions [39], [61]. In
the first reaction network, guaiacol decomposes to the single ring structures
such as o-cresol and benzene or it directly gasifies in the subcritical water.
Crosslinking between decomposition products produces heavier compounds,
such as diphenyl (which is a model compound for TOC). Diphenyl and benzene
further crosslinks the active sites of the ring structure that results in char
formation. Diphenyl undergoes to a direct gasification via a parallel reaction.
The reaction network of guaiacol in subcritical water is depicted in Figure 3.5.
Figure 3.5 Guaiacol decomposition scheme in subcritical water [61]
In supercritical water decomposition network is generated as shown in
Figure 3.6. In this region, the research findings of Yong et al. in separate works
[39], [61] were integrated in order to obtain a detailed reaction network for the
lignin depolymerization in supercritical water. Guaiacol decomposition reactions
are either ionic or radical. Radical reactions show good accordance with the
Arrhenius equation even though, some kinetic data could not be fitted to the
Arrhenius relation. The physical property change of water from the sub- to
supercritical region is the main reason of the occurrence non-Arrhenius behavior
of the ionic reactions.
37
Figure 3.6 Guaiacol decomposition scheme in supercritical water kgu.ch, khu.ga, kgu.oc, kgu.c, kgu.t, kc.oc,
kt.ch from [61]; kc.t, kp.c, kp.t, kp,ga, kp.ch, kt.b, kb.t, kb.p, kb.ga, kb.na, kna.ch, kb.ch from [39]
38
3.4
Acid Decomposition Pathway:
In the liquid effluent of the biomass conversion studies organic acids are
detected as the secondary hydrolysis products, which are accepted to be the
main gaseous product source. Acid decomposition to the gas phase mostly takes
place at elevated temperatures higher than 370 ℃, below which they are almost
stable. Literature was missing decomposition data of glycolic acid and levulinic
acid. 2 carbon containing glycolic acid was proposed to hydrolyze to form
carbondioxide and hydrogen and 5 carbon containing levulinic acid decomposes
to form tertiary intermediates (lactic acid and acetaldehyde). Rest of the
pathway was built according to the findings in the literature, shown in Figure 3.7
Figure 3.7 Organic acid gasification scheme in supercritical water. kfa.ga1, kfa.ga2 [63]; kaa.ga [64];
kwshs.ga, kpa.ga, kmf.aa [57]; lactic acid reactions [65]
39
Water gas shift (WGS) and methanation reactions given in Equation 3-1 and
Equation 3-2 are the main gas phase reactions that take place between the gas
species after they are formed from either intermediates or pyrolytic gasification
of guaiacol and derivatives [47].
WGS
𝐶𝑂 + 𝐻2 𝑂 ↔ 𝐶𝑂2 + 𝐻2
Equation 3-1
Methanation
𝐶𝑂 + 3𝐻2 ↔ 𝐶𝐻4 + 𝐻2 𝑂
Equation 3-2
40
3.5
Kinetics Data and Arrhenius Parameters
Cellobiose decomposition reaction rates were predicted in the
experemintal study of Kabyemela [41], by assuming 1st order decompsition type
rate equations and kinetic data were calculated accordingly. Kinetic data listed in
Table 3.1 was in a good accordance with the Arrhenius relation (Equation 3-3) in
the given temperature range. Pressure difference in the last set of parameters
was accepted to have negligible significance for the reactions that take place in
near-critical water; the resulting parameters are shown in Table 3.3.
Table 3.1 Cellobiose decomposition rate constants
Rate constant, s-1 300 ℃
25 MPa
Cellobiose
0,21
(overall)
k1
0,03
k2
0,04
k3
0,14
kge.g
0,15
kgg.g
0,17
conditions
350 ℃
25 MPa
400 ℃
30 MPa
Ref.
1,06
4,9
[51]
0,05
0,11
0,9
0,9
1,1
0,25
0,35
4,3
3,7
3,5
[51]
[51]
[51]
[51]
[51]
Glucose and fructose decomposition reaction rate constants were derived by Kabyemela et
al. [43] from his group’s own experimental findings as listed in
Table 3.2. In the given temperature range reactions are in a good accordance
with Arrhenius law. However, the ionic mechanism of fructose decomposition
reactions and glucose to fructose isomerization reaction may result in deviation
in supercritical water at elevated temperature, product composition results of
the preliminary simulation runs show that, almost complete conversion of
glucose and fructose take place in a duration less than the residence time spent
in supercritical water. Hence, for the kinetic parameter calculation of this set of
rate constants were done with the assumption of Arrhenius behavior, results are
listed in Table 3.3. Furfural to char reaction rate data were not available, even
though possible char formation was reported [3]. Kinetic rates of furfural to char
reaction was assumed to be the same as phenol to char reaction (in supercritical
water only), owing to the similar structure of the compounds.
41
Table 3.2 Glucose decomposition rate constants
Rate constant,
s-1
kg.a
ke.acid
ka.acid
kf.gly
kf.acid
kf.e
kg.e
kg.gly
kg.f
kgly.dih
kgly.p
kdih.gly
kdih.p
conditions
350 ℃,
25 MPa
0,02
0,55
0,04
0,6
0,7
0,8
0,95
0,2
0,64
1,38
0,94
0,2
0,02
300 ℃,
25 MPa
0,01
0,1
0,01
0,1
0,18
0,1
0,21
0,05
0,2
0,4
0,19
0,03
0,01
400 ℃,
30 MPa
0,08
5
0,31
6,5
10,4
8
18,1
1
7
7,15
4,6
1,04
0,08
Ref.
[53]
[53]
[53]
[53]
[53]
[53]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
Temperature dependence of the reaction rates was expressed with the
well-known Arrhenius equation unless it was found to deviate from Arrhenius
behavior; for simulating purposes Activation energy (Ea) and pre-exponential
factor (A) kinetic parameters were derived.
−𝐸𝑎
Equation 3-3
−𝐸𝑎 1
∙ + ln(𝐴)
𝑅 𝑇
Equation 3-4
𝑘 = 𝐴 ∙ 𝑒 (𝑅𝑇)
ln(𝑘) =
Reaction rate constant dependence on temperature was tested with
checking the linearity between the natural logarithm of reaction rate constant (ln
k) and reciprocal temperature (1/T). The calculation method is illustrated in
Figure 3.8, and resulting parameters including the one shown below are listed in
Table 3.3. Cellobiose and glucose decomposition kinetic parameters were also
investigated by Cantero et al. [54] by using the same rate constants as found in
the literature [51], [53], [3]. Calculated values and Cantero’s values were in
complete agreement, which pointed out the precision of the calculations.
Glycolaldehyde to acid decomposition rate parameters were not found in the
literature. Reported parameters were assumed to be the same as erythrose to
acid decomposition reaction due to the similar bonding types. Arrhenius
parameters of acid formation reactions were directly obtained from the
literature [54] data as presented in Table 3.3.
42
1000/T(K) vs. ln(k)
0
1.45
1.5
1.55
1.6
1.65
1.7
1.75
1.8
-0.5
ln (k)
-1
-1.5
y = -11.51x + 17.016
R² = 0.9921
-2
-2.5
-3
-3.5
1000/T(K)
Figure 3.8 Arrhenius relation of glucose to glyceraldehyde decomposition reaction
Table 3.3Arrhenius Parameters of cellobiose and glucose decomposition
Symbol
1
2
3
ge.g
gg.g
g.a
f.gly
f.e
g.e
g.gly
g.f
gly.dih
gly.p
dih.gly
dih.p
fu.ch
e.acid
a.acid
p.acid
f.acid
glyo.acid
Ea (kJ/mol)
66,89
69,30
108,60
106,10
110,50
65,93
133,04
140,44
141,34
95,69
112,69
154,36
82,56
77,32
88,65
87,90
124,72
109,29
94,00
128,63
124,72
A, s-1
3,1E04
7,8E04
1,4E09
3,6E44
1,4E08
9,0E03
1,2E11
5,3E11
1,2E12
2,5E07
3,0E09
1,5E13
7,6E06
4,6E05
1,8E07
1,4E04
2,1E10
8,0E07
6,59E07
7,4E10
2,1E10
Ref.
[54]
[54]
[54]
[54]
43
5.lf
5.ff
95,60
114,8
8,0E07
7,1E09
Promdej and Matsumura [3] studied the temperature dependence of
glucose decomposition reactions in sub- and supercritical water, computed rate
constants according to the compositional data of the experiment for
temperatures between 300 ℃ and 460 ℃. In the kinetic simulation of the current
work, 5-HMF formation reaction rate constants were used as displayed in Table
3.4.
Table 3.4 Glucose decomposition rate constants continued [3]
Rate
constant,
s-1
kg.5
kf.5
300 ℃,
25 MPa
350 ℃,
25 MPa
conditions
400 ℃,
30 MPa
425 ℃, 450℃,
25 MPa 25 MPa
460 ℃,
30 MPa
6,0E-03
1,620E01
3,27E-02
2,15E-01
0,3
0,132
0,15
3,30E-01
3,82E-01
0,118
0,0932
0,087
In supercritical water, reaction rate constants of glucose and fructose
dehydration reactions to form 5-HMF do not obey Arrhenius law due to the
change in water properties as can be followed from Table 3.4. Kinetic parameters
of 5-HMF formation from glucose and fructose were calculated for sub- and nearcritical region only, by expecting the rapid complete conversion in that region, as
reasoned in paragraph 2.1.1. Resulting Arrhenius parameters applied to the
model as summarized in Table 3.5
Table 3.5 Arrhenius Parameters of glucose decomposition continued
Symbol
g.5
f.5
Ea, kJ/mol
114,4
42,3
A, s-1
1,5E08
1,2E03
Xylose decomposition pathway and resulting rate constants were handled
separately in sub- and supercritical water, owing to the availability of the
literature data.
In sub-critical water the main decomposition products of xylose are
parallel reaction products, furfural and methyl formate-glyceraldehyde solution,
for temperatures between 180 and 220 ℃ and under pressure of 10 MPa [56].
44
Table 3.6 Xylose decomposition rate constants in subcritical water
Rate
constant,
s-1
koverall
kxy.fu
kxy.gm
kfu.aa
180 ℃ , 190 ℃,
10 MPa
10 MPa
conditions
200 ℃ , 210 ℃ , 220 ℃ ,
10MPa
10 MPa
10MPa
Ref.
0,00450
0,00322
0,00128
3,28E-4
0,02420
0,0119
0,0123
6,47E-4
0,00960
0,00583
0,00377
4,57E-4
0,03420
0,0192
0,0150
7,93E-4
0,0653
0,0356
0,0297
1,22E-3
[56]
[56]
[56]
[56]
Kinetic parameters of the xylose decomposition reactions in subcritical water are tabulated
in
Table 3.7, according to which, D-xylose stays almost stable under the subcritical
conditions.
Table 3.7 Arrhenius parameters of d-xylose decomposition subcritical water
Reaction Type
kxy.fu
kxy.gm
kfu.aa
Ea, kJ/mol
76,6
153,8
24,2
A, s-1
1230
7,38E08
2,95E-14
In supercritical water for elevated temperatures, the simplified
gasification pathway of xylose was found to be more precise than detailed
decomposition pathway in terms of product compositions, according to which
rate constants and Arrhenius parameters were derived by Goodwin and
colleagues [57] (see Table 3.8).
Table 3.8 Arrhenius parameter of d-xylose decomposition supercritical water
Symbol
Overall
xy.wshs
fur.wshs
wshs.ga
Ea, kj/mol
147,5
154,7
100,5
142,7
A, s-1
1,3E13
6,6E14
1,7E6
3,5E8
Ref.
[57]
[57]
[57]
[57]
Guaiacol depolymerization pathway was elucidated and 1st order reaction
rate constants (see
45
Table 3.9) were fitted to the composition results of the experiment, performed
by Yong et al. [61] for temperatures between 300 and 450 ℃ at a pressure of 25
MPa.
Table 3.9 Lignin decomposition rate constants sub- and supercritical water [61]
Rate
Constant,
s-1
kgu.t
kgu.ga
kgu.ch
kgu.b
kgu.c
kgu.oc
kc.t
kt.p
kp.ch
koc.t
kt.ch
kt.b
kt.ga
kb.ch
300 ℃
2 MPa
conditions
350 ℃ , 370 ℃ , 390 ℃ , 420 ℃ , 450 ℃ ,
250 MPa 250 MPa
250 MPa 250 MPa 250 MPa
9,54E-02
2,45E-05
0
1,92E-04
2,06E-04
8,52E-05
3,49E-02
6,76E-04
0,00E+00
1,21E-04
2,36E-05
5,09E-05
1,84E-03
1,34E-05
9,58E-02
7,74E-04
0
3,30E-04
9,01E-04
9,32E-02
4,38E-02
1,39E-03
0,00E+00
3,41E-03
1,91E-04
4,45E-04
2,50E-03
7,28E-03
1,28E-01
9,19E-04
0
1,85E-03
1,31E-03
1,37E-04
4,40E-02
1,54E-03
0,00E+00
3,99E-03
1,61E-04
2,31E-04
4,73E-05
7,81E-03
1,35E-01
3,62E-03
2,01E-04
0,00E+00
2,60E-03
1,01E-02
0,00E+00
1,26E-03
8,41E-04
1,38E-02
1,36E-03
0,00E+00
3,40E-03
0,00E+00
1,85E-01
8,51E-03
8,64E-03
0,00E+00
9,07E-02
1,94E-02
0,00E+00
9,41E-04
8,99E-04
2,60E-03
5,66E-03
0,00E+00
4,25E-03
0,00E+00
3,15E-01
1,55E-02
1,08E-02
0,00E+00
2,53E-02
3,54E-02
0,00E+00
8,84E-04
1,00E-03
2,50E-03
7,05E-03
0,00E+00
6,02E-03
0,00E+00
Guaiacol to o-cresol and guaiacol to catechol reaction rates show a decrease with
temperature beyond the critical point. Slow reacting lignin derivatives, were expected to be present
in the supercritical water and presence of these reactions in the set is considered to be important;
therefore, average value of the reaction rates (that deviate from Arrhenius) were calculated and
integrated to the model with negligible activation energy. Hence, neither a favoring nor a
suppressing temperature effect on these specific reactions was assumed in supercritical water, with
the purpose of favoring radical type of reactions over the ionic ones in the elevated temperatures.
The extent of the error margin due to the non-suppression
of the temperature increase
for ionic reactions will be evaluated in the discussion section. Resulting
kinetic parameters can be viewed in
Table 3.10 and
46
Table 3.11.
Table 3.10 Arrhenius Parameters of guaiacol decomposition subcritical water
Symbol
gu.t
gu.ga
gu.b
gu.c
gu.oc
c.t
c.oc
t.p
oc.t
t.ch
t.b
t.ga
b.ch
Ea, kJ/mol
48,28
168,75
84,64
82,43
0
10,87
8,48
37,51
162,86
92,77
79,79
18,20
299,45
A, s-1
1067,1
6,7E+10
8284,2
6833,6
3,11E-02
0,3
0,005
1,8
9,5E=10
7508,6
1129,9
0,08
3,4E+22
Table 3.11 Arrhenius Parameters of guaiacol decomposition supercritical water
Symbols
gu.t
gu.ga
gu.ch
gu.c
gu.oc
c.oc
t.p
p.ch
oc.t
t.ch
Ea, kJ/mol
56,1
96,8
267,9
0
83,3
18,5
0
11,5
0
110,4
A, s-1
3377,9
158102,7
4,4E+17
3,9E-02
37049,1
0,06
1,0E-03
0,007
6,3E-03
815046,1
47
t.ga
37,8
3,3
Main guaiacol derivatives phenol and benzene were further analyzed for
decomposition reaction pathway in supercritical water for temperatures
between 370 and 450 ℃ under pressure of 25MPa, and pathway rate constants
were found accordingly (See Table 3.12) [39]
Table 3.12 Phenol- benzene decomposition reaction rate constants supercritical [39]
Reaction Rate,
s-1
kb.p
kb.na
kb.ga
kb.ch
kb.t
kp.ga
kp.t
kp.c
kp.ch
kc.t
kna.ch
kt.ga
370 ℃, 25 MPa
conditions
400 ℃, 25 MPa
450 ℃, 250 MPa
3,07E-04
9,04E-04
2,05E-04
3,86E-04
2,99E-04
7,03E-04
2,43E-02
0,00E+00
7,86E-04
0,00E+00
1,90E-03
1,77E-02
4,52E-04
1,78E-03
3,50E-04
7,60E-04
3,15E-04
1,69E-03
2,93E-02
1,67E-04
3,14E-03
8,70E-03
2,35E-03
1,90E-02
1,01E-03
3,83E-03
4,24E-04
1,44E-03
8,18E-04
2,52E-03
3,92E-02
5,03E-04
5,24E-03
1,57E-02
4,05E-03
1,92E-02
It was reported that ionic and radical reactions are likely to take place
together for the phenol-benzene decomposition system [39]. Even though
Arrhenius behavior was followed in the most cases, very low pre-exponentials
indicate the suppression of the ionic reactions (e.g. dehydration, hydrolysis etc.)
of the derivative compounds in supercritical water, which are likely to be present
in the outlet stream of the simulation.
Table 3.13 Arrhenius parameter of phenol-benzene decomposition in supercritical water
Symbol
b.p
b.na
b.ga
b.ch
Ea, kJ/mol
15,6
391,2
0,1
50,9
A, s-1
58,1
69,2
33,6
62,7
48
b.t
p.ga
p.t
p.c
p.ch
c.t
na.ch
t.ga
3,5
54,8
1,8
1407,1
13718,5
44,4
1,9
27,7
50,8
59,5
23,1
89,2
87,9
47,8
37,2
3,7
Formic acid gasification kinetic parameters for 2 parallel reactions were
calculated from the experiment findings of Yu et al. [63] for temperatures
between 320 and 400 ℃, the results given in Table 3.14, formic acid is almost
stable in subcritical water, even though rapid gasification reactions occur in
supercritical water at elevated temperatures.
Table 3.14 Arrhenius parameters of formic acid decomposition [63]
conditions
subcritical
supercritical
A
Eact, kJ/mol
A
Eact, kJ/mol
f.ga1
1,6E06
85,01
4,8E12
168,20
f.ga2
4,6E-05
NA
3,40E17
2,44E02
Arrhenius parameters of the acetic acid and propionic acid decomposition
reactions in supercritical water were reported, hence they were directly taken
from the literature [57], [64] (See Table 3.15). Acetic acid and propionic acid
reactions are favored in the elevated temperature region in the supercritical
water (𝑇 > 400 ℃), in subcritical region (for short residence time) they were
accepted as stable compounds.
Table 3.15 Arrhenius parameters of acetic and propionic acid decomposition
Ea, kJ/mol
Acetic Acid (aa.ga)
94
Propionic Acid (pa.ga) 89,4
A, s-1
2,5E+4
1,4E+5
Ref.
[51]
[61]
Lactic acid decomposition rate data and reaction pathway was
studied in near- and supercritical water for temperatures between 350 and
420 ℃ under 25 MPa pressure [65], kinetic data for the proposed reaction
pathway is tabulated in
Table 3.16
49
Table 3.16 Lactic acid decomposition reaction rate constants [65]
Reaction Rate, 350 ℃,
s-1
25 MPa
Global
1,80E-03
koverall
1,10E-03
kla.aa
2,00E-04
kaa.la
9,40E-04
kla.aal
9,40E-02
kaal.ac
9,80E-04
kaa.hpa
5,00E-03
khpa.aa
7,99E-04
khpa.ga
2,43E-03
conditions
385
25 MPa
5,00E-03
2,79E-03
3,76E-04
4,22E-03
1,56E-03
1,18E-03
1,83E-03
8,98E-04
1,40E-03
Ref.
℃ 420
25 MPa
1,04E-02
4,17E-03
3,95E-04
1,68E-02
2,07E-03
4,78E-03
7,26E-02
1,59E-03
9,30E-05
℃
[65]
[65]
[65]
[65]
[65]
[65]
[65]
[65]
[65]
Overall lactic acid decomposition obeys the Arrhenius behavior in nearand supercritical water, even though formation reactions of acetic acid from
acetaldehyde, acrylic acid from 3-HPA and glycolic acid-methanol solution from
3-HPA deviated from the Arrhenius relation. Average rate constants in the given
temperature range for the indicated reactions were calculated by assuming
negligible Activation energy, with the reasoning given in the guaiacol paragraph.
Resulting kinetic parameters are displayed in
Table 3.17.
50
Table 3.17 Arrhenius parameters of lactic acid decomposition supercritical water
Symbols
overall
la.aa
aa.la
la.aal
aal.ac
aa.hpa
hpa.aa
aa.pa
hpa.gly
Ea, kJ/mol
90,2
68,8
35,4
147,7
0
80,1
0
34,9
0
A, s-1
6,71E04
6,89E02
2,00E-01
2,37E09
3,25E-02
4,13E03
2,65E-02
6,00E-01
1,31e-03
WGS reaction and methanation reactions were identified as main gas
phase reactions. Water gas shift reaction was found to shift forward due to the
presence of high water concentration resulting in very low CO concentration
[47]. Methanation reaction effect on methane composition was found extremely
low and methane formation was mainly linked to the glucose-xylose
intermediates and guaiacol [47]. For the current study, one way WGS reaction
was accepted and methanation reaction influence on reaction kinetics was
neglected by aiming the consistency with experimental kinetic work. According
to the experimental results of Sato, activation energy and pre-exponential factor
of forward WGS reaction in non-catalytic hydrothermal conditions are 116
kJ/mol and 380189 s-1, respectively [66].
51
4 Integration of Kinetic Data Set to the Reactor SimulationTest Run & Validation
4.1
Integration of Kinetic Data Set to the AspenPlusTM Simulation
AspenPlusTM is a widely used process modeling tool for power, chemical,
petrochemical, oil and gas industries for conceptual design, performance analysis
and optimization purposes. It offers the world’s largest physical property
database for conventional chemicals, electrolytes, solids and polymers, which is
annually updated by U.S. National Institute of Standards and Technology (NIST).
In addition, software has a diverse unit operation library including pump,
compressor, valve, tank, heat exchanger, reactor (CSTR, PFR, Mixed Batch), flash
column, absorber, filter, crystallizer, etc. Features of the software are suitable for
the modeling of complex reacting systems with numerous chemical compounds
at high pressure and temperature conditions. Furthermore, the scope of the
work required the process flow-sheet build up with several units that are
combined with reactors in a steady state system. This software was chosen in
order to compile the outcome of kinetic approach to the SCWG process and to
monitor the performance of the system at different operating parameters, in
time efficient and precise way.
A process simulation study was initiated by integrating kinetic data
derived to the reaction database of the software. Based on kinetic studies
obtained from the literature, biomass degradation was modeled by 1st order
decomposition type reactions. Power law expression (built in the software)
given in Equation 4-1 was applied for the reaction set, which relates the rate of
reaction to the temperature, concentration of the reactant compound and
stoichiometric coefficient.
𝑟 =𝐴∙𝑒
(
1 1
−𝐸𝑎⁄
𝑅 )∙[𝑇 −𝑇0 ]
𝑁
∙ ∏ 𝐶𝑖 𝛼𝑖
Equation 4-1
𝑖=1
52
In order to achieve mass balance closure for guaiacol reactions,
stoichiometric coefficient of the reactants were manipulated, even though the
rate equations were kept as 1st order which was recorded as a limitation of the
simulation, extent of the effects can be viewed in validation part. Rate constants
were generally in a fair accordance with the Arrhenius law, hence the effect on
results are expected to be minimal. In supercritical water, several non-Arrhenius
behavioral reaction rate constants were preset, inserted to the software with the
average value found in the given temperature range.
Reactions and correspondent rate parameters were separated into two
for sub- and supercritical regions due to the presence of separate studies for Dxylose and guaiacol. Having two different main reaction sets improves the
running efficiency of the simulation program by splitting into liquid and vapor
reacting phases.
The set of reactions were applied to the T-Spec type RPlug reactor of the
software with the assumption of radial mixing and process stream temperature
specification for axial direction. At least 2 tubular flow reactors were used in the
set of simulations due to the presence of 2 reaction sets for SCWG reactions (see
Annexes A and B). The feed stream was assumed to be in liquid phase with the
knowledge of high solubility of the model compounds in hot and compressed
water.
For thermodynamic and transport properties estimation of the
simulation, selections of property methods are offered. In this study kinetic
approach was accepted hence reaction equilibrium calculations were not
performed, however mixing behavior of the compounds and equilibrium partial
pressures are important for reactor and flash separator calculations.
Among the various selections in AspenPlusTM property method menu,
Ideal gas law EOS is the simplest method but insufficient of vapor-liquid
equilibrium calculations near critical temperature, 373 ℃. Peng- Robinson (PR)
and Redlich-Kwong (RK) property methods belong to the cubic EOS found
insufficient for fluid property calculation at elevated temperatures, these EOS’ s
are recommended for low to moderate pressures [10].
For the SCWG process simulations, Soave Redlich-Kwong (SRK) physical
property method was selected because of its consistency in fluid property
estimations near critical conditions. The general SRK expression used as
property method is expressed as follows:
𝑃=
𝑅𝑇
𝑎(𝑇)
−
𝑣̃ − 𝑏 𝑣̃(𝑣̃ + 𝑏)
Equation 4-2
𝑎
𝑎𝑖
𝐺𝐸
𝑏
= ∑ 𝑥𝑖
− 1,546 ( + ∑ 𝑥𝑖 𝑙𝑛 )
𝑏𝑅𝑇
𝑅𝑇𝑏𝑖
𝑅𝑇
𝑏𝑖
𝑖
Equation 4-3
𝑖
𝑅 2 𝑇𝐶2
𝑎 = 0,43
𝛼
𝑃𝐶 𝑖
Equation 4-4
53
𝑏 − 0,087
𝑅𝑇𝐶
𝑃𝐶
Equation 4-5
0,5
0,5 2
0,5 3
𝛼𝑖 = [1 + 𝑐1 (1 − 𝑇𝑟,𝑖
) + 𝑐2 (1 − 𝑇𝑟,𝑖
) + 𝑐3 (1 − 𝑇𝑟,𝑖
) ]
2
Equation 4-6
For a co-volume parameter (b), linear mixing rule (Gmehling approach) is
applied for a definition of the a and b parameters. Where 𝐺 𝐸 stands for a Gibbs
free energy term, 𝑇𝑟 refers to the reduced temperature and 𝑐1,2,3 are the MathiasCopeman constants.
4.2
Results of the Reactor Test-Run
In the preliminary runs for the reactor response verification, 2 isothermal
plug flow reactors were used. The first reactor operates at 25 MPa and 370 ℃
and the second reactor operates at 25 MPa and 650 ℃. The main objective was to
observe the consumption and formation rates of the organic compounds within
the short residence time and the gas phase reaction trend in the 1st and 2nd
reactor, respectively. Table 4.1 shows the parameters selected for the
preliminary simulation runs.
Table 4.1Simulation set parameters for preliminary run
Biomass Biochemical Composition
(Corn Cob)
Cellulose
Hemicellulose
Lignin
Ash
Feed Flow Rate
Biomass Load
Residence time
1st reactor
2nd ractor
(wt. %), dry
52
32
15
1
kg/hr
100
(wt. %)
4
s
3,4
6,7
According to Figure 4.1, cellobiose, model compound for cellulose
decomposes rapidly in the presence of hot compressed water in the subcritical
tubular flow reactor. The main decomposition products glycosyl-glyceraldehyde,
glycosyl-glycolaldehyde and glucose are formed simultaneously. Then, glucose
molar fraction starts decreasing slightly which indicates the higher rate of the
overall glucose decomposition reactions as compared to the glucose formation
54
under the given process conditions. Molar fraction of the glycosylglyceraldehyde and glycosyl-glycolaldehyde, pyrolysis products of the cellobiose
first increases then becomes steady, which is an indication of equal formation
and decomposition reaction rates of the compounds. Fructose molar fraction
develops in the beginning and then the fructose decomposition reactions
dominate in the near-critical region, which results in a decrease of the compound
similar to glucose.
0.0016
0.0014
Molar Fraction
0.0012
0.001
CELLOB
0.0008
GLYLGY
0.0006
GLYERY
GLUCOS
0.0004
FRUCT
0.0002
0
0
1
2
3
Reactor Length (m)
4
5
Figure 4.1 Simulation results of cellobiose decomposition products in isothermal subcritical reactor at 370
℃
Decomposition products’ formation and decomposition trends are shown
in Figure 4.2. Erythrose and glyceraldehyde forms instantly since while
anhydroglucose, dihydroxyacetone, pyruvaldehyde and 5-HMF form with a delay
confirming the reaction network proposed for the cellobiose decomposition.
These compounds are further decomposed to the acids, furfural or acetaldehyde.
1.20E-03
Molar Fraction
1.00E-03
ERYTH
8.00E-04
GLYCER
GLYCOL
6.00E-04
ANHYDRO
4.00E-04
DIHYDR
PYRUVAL
2.00E-04
HMF
0.00E+00
0
1
2
3
4
Reactor Length (m)
5
55
Figure 4.2 Simulation results of glucose decomposition products in isothermal subcritical reactor at 370 ℃
Using xylose as a starting compound, kinetics of hemicellulose
decomposition was examined under given conditions. According to Figure 4.3,
xylose is a stable compound and only a slight change in the molar fraction is
observed, which indicates that for hemicellulose decomposition reactions to be
favored supercritical condition is required.
0.0025
Molar Fraction
0.002
0.0015
XYLOSE
METFOR
0.001
FURFURAL
0.0005
0
0
1
2
3
4
Reactor Length (m)
5
Figure 4.3 Simulation results of xylose decomposition products in isothermal subcritical reactor at 370 ℃
Decomposition of lignin in subcritical water was evaluated by performing
the simulation for guaiacol with the built model. Guaiacol conversion reaches
above 50% producing and bi-phenyl as a main compound. As shown in Figure
4.4, catechol, o-cresol and benzene formation are almost negligible due to the
higher activation energy of the reactions, which will be favored in supercritical
conditions.
56
0.0012
Molar Fraction
0.001
0.0008
GUAIACOL
DIPHENYL
0.0006
CATECHOL
0.0004
OCRESOL
BENZENE
0.0002
0
0
1
2
3
Reactor Length (m)
4
5
Figure 4.4 Simulation results of guaiacol decomposition products in isothermal subcritical reactor at 370 ℃
Organic acids are the main decomposition products of the biomass under
hydrothermal conditions as hydrolysis products of aldehydes, ketones, furfurals
and other organic intermediates. Figure 4.5 presents the molar composition
development along the reactor length. The highest composition organic acids are
lactic acid and acetic acid, both of which increase in composition almost steadily.
Composition trend shown in Figure 4.5 is a result of reaction set applied for the
subcritical condition, which disregards the hydrolysis of the acids below 370 ℃.
3.50E-03
Molar Fraction
3.00E-03
2.50E-03
2.00E-03
LACTIC
1.50E-03
ACETIC
PROPIO
1.00E-03
ACRYLIC
5.00E-04
0.00E+00
0
1
2
3
Reactor Length (m)
4
5
Figure 4.5 Simulation results of organic acid decomposition products in isothermal subcritical reactor at
370 ℃
Gaseous product formation in the second reactor, which operates at 650
℃ and 25 MPa is shown in Figure 4.6. Composition trends of the gaseous
57
Mole Fraction
products are in agreement with the kinetics of the gasification reactions and also
the water gas shift reaction since along the tube hydrogen, carbondioxide and
methane molar compositions develop while the carbonmonoxide composition
increases at the beginning then starts to diminish as time counts. Small
fluctuation peaks at specific instants are estimated to be the results guaiacol and
derivatives' pyrolytic gasification reactions; even though, the trend of gas
compositions are mainly linked to the forward WGS reaction.
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
H2
CO2
CO
CH4
C2H8
0
5
10
Reactor Length (m)
15
20
Figure 4.6 Simulation results of xylose decomposition products in isothermal supercritical reactor at 650 ℃
58
4.3
Validation of the simulation
Process gasification efficiency in the whole temperature region, up to 650
℃ and also the intermediate yield response to the residence time and
temperature variations are the results to be validated. The detailed and high
contented set of experiments of Lu et al. [9], enabled the validation of the
simulation system results in a range of pressure (20-30 MPa), residence time
(15-47 s) and also the feedstock. Apart from the fruitful content of Lu and
colleagues investigation, the temperature profile of a reacting medium was not
described quantitatively. High gasification efficiency (88 – 105 %) of the
experimental work [9] for short duration indicates rapid heating of the reacting
medium, since it was found out earlier that gasification reactions are mostly
favored at elevated temperatures and residence time spent under high
temperature conditions directly influences the gasification efficiency.
Accordingly, it was assumed that biomass slurry is rapidly preheated to 200 ℃
(~1 s) and spends 5 s in the subcritical reactor before reaching to the nearcritical temperature 370 ℃ (see Figure 4.7).
400
Temperature ( C )
350
300
250
200
t vs. T
150
100
50
0
0
1
2
3
4
Residence time (s)
5
6
Figure 4.7 Temperature profile in subcritical reactor
Supercritical reactor converges to the isothermal reactor with preheat to
the maximum temperature of 650 ℃ within the first 1/10 th of the residence
time and temperature stays constant during the rest of the operation. Variation
59
in overall residence time for comparison studies applied only to the supercritical
reactor -at which almost all gas production occurs- by varying the tube length.
Resulting heating trajectory and temperature versus time results table can be
viewed in and Figure 4.8.
700
Temperature (C )
600
500
400
300
t vs. T
200
100
0
0
5
10
15
Residence time (s)
20
25
Figure 4.8 Temperature profile in supercritical reactor
Another important design parameter, flow type of the fluid inside the
reactor is of great importance since biomass conversion in water consists mostly
of diffusion driven reactions. 6 to 9 mm inner diameter reactor pipes with a highpressure stream [9], tubular flow reactor with the plug flow assumption were
found to be suitable, which is possible to simulate using AspenPlusTM RPLUG
Reactor. The experimental setup of Lu et al., is presented in Figure 4.9 in order to
give more insight regarding the SCW gasification process in laboratory scale. The
setup is mainly equipped with a water tank connected to the pumping system,
two feeding tanks (one is at the process pressure while the other is atmospheric
for a fresh feed), stainless steel tube reactor, external heating and
thermocouples, outlet gas cooler, back pressure regulator valve liquid gas
separator and outlet gas and liquid quantifying units. The feedstock used in
experiments is mixed with carboxy methyl cellulose (CMC) in equal quantity in
order to increase the viscosity of the fluid for pumping efficiency. In this study
added CMC was accepted to have similar reacting behavior and physical
properties with cellobiose.
60
Figure 4.9 Scheme of the experimental setup used in Lu’s work [9]
4.3.1 Effect of Residence Time
Figure 4.10 shows the change in molar flow of the product gases with
residence time. Residence time variation was performed by altering reactor tube
length. For 27 s of operation, total tube length of 6,3 m was required with the
input stream of 100 kg/hr. Other system parameters are set to the experimental
ones, which are represented in Table 4.2.
Table 4.2 Simulation operating parameters; comparisons for varying residence time
Pressure (MPa)
Temperature (K)
Biomass feed load (%)
Residence time (s)
Feedstock
25
923
4
15-47
Wood sawdust +CMC
The molar flow of hydrogen, carbondioxide and methane tend to increase
while carbonmonoxide reduces in time. The results show that gasification
reactions favor the increase in residence time and WGS reaction dominates the
gas phase system (See Figure 4.10).
61
Molar Flow (kmol/hr)
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
H2
CO2
CO
CH4
10
20
30
Residemce time (s)
40
50
Figure 4.10 Effect of residence time on product gas molar flow
The validation study was started by comparing the conversion
efficiencies; gasification efficiency and carbon gas efficiency results of the
process simulation with the to-be-validated study [9]. GE refers to the
percentage of the gaseous phase mass outlet to the dry biomass inlet. CE stands
for the percentage of the number of moles of the carbon in the gaseous outlet to
the biomass inlet to indicate the hydrogen production capacity of the system.
Gasification efficiency (GE) and Carbon gas efficiency (CE) are expressed in
Equation 4-7 and Equation 4-8, respectively.
𝐺𝐸 =
𝑚𝑔,𝑜
∙ 100%
𝑚𝑏𝑖𝑜,𝑑𝑟𝑦
Equation 4-7
𝑛𝑐,𝑔
∙ 100%
𝑛𝑐,𝑏𝑖𝑜
Equation 4-8
𝐶𝐸 =
GE and CE tend to increase with the residence time, which is simply
explained by enhanced production of gas products by serving more time to the
gasification reactions. Average relative errors for the GE and CGE are found to be
13% and 8%, respectively. Besides the quantitative findings, trend of the
efficiencies also show good accordance as can be viewed in Figure 4.11.
62
Efficiency (%)
120
100
80
CE
60
CE*
GE
40
GE*
20
10
20
30
Residence time (s)
40
50
Figure 4.11 Comparison between simulation results GE, CE and experimental results GE*, CE* for residence
time between 15 and 47 seconds
Gaseous species compositions for the given residence time interval were
also of great importance, by specifying the hydrogen or methane concentrations
in the product gas, which are the main energy containing compounds at the
process outlet. Figure 4.12 shows the yields of the main gaseous products of the
process; hydrogen, carbondioxide, carbonmonoxide. The yield of each product
gas was calculated as shown in the Equation 4-9.
𝑌𝑖 =
𝑛𝑖
𝑚𝑏𝑖𝑜,𝑑𝑟𝑦
∙ 100%
Equation 4-9
Where 𝑛𝑖 refers to the molar flowrate of a particular product gas and
𝑚𝑏𝑖𝑜,𝑑𝑟𝑦 refers to the mass flowrate of dry biomass to the reactor. A very small
amount of ethane (10-8 mol gas/kg biomass) was detected but not shown in the
chart, since Lu et al. did also not examine it for this study. Disagreement for
short residence time is estimated to be a result of reduction in supercritical
reactor (where mainly gas production takes place) while keeping subcritical
reactor length constant.
Hydrogen, carbon dioxide, and carbon monoxide formation with respect
to residence time show the same trend as Lu’ s; yield of the components are also
in a fair accordance, see Figure 4.12. Methane yield, on the other hand is higher
than the experimental study, which suggests that in the experiment methane
forms slower than the simulation conditions especially in the supercritical
region. Hydrogen yield in the outlet stream, contrarily, is slightly lower than the
validation results indicating the slower rate than the experiment. Good
agreement in gasification efficiency but slight disagreement in gas compositions
with Lu’s experiments suggest that gas forming reaction rates of the simulation
are precise even though gas forming reaction equations are not fully correct.
Lignin gasification path reaction stoichiometric coefficients were derived by
performing RGibbs reactor at temperature of operation (for this case 650 ℃)
since stoichiometric data was not published; which is the most likely reason of
the disagreement. It is noteworthy that hydrogen, carbon dioxide and methane
63
composition increase time while the carbon monoxide concentration reduces by
following the similar trend with the experimental results.
Yield (mol gas/kg bio,dry)
22
H2
17
H2*
CO2
12
CO2*
7
CO
CO*
2
-3 10
20
30
Residence time (s)
40
50
CH4
CH4*
Figure 4.12 Comparison between product yield results of the simulation and experimental results (*), for
residence time between 15 and 47 seconds
4.3.2 Effect of Pressure
Comparison of the gasification efficiencies and gas yields under the
influence of varying operating pressure are shown in Figure 4.13 and Figure
4.14, respectively. In order to obtain the response of the system to the pressure,
sensitivity analysis of simulation was performed with the model analysis tool of
the AspenPlusTM. Gasification results were calculated by using the amount of
product gases for specific pressure levels between 20 and 30 MPa. Simulation
parameters are set to the experimental ones and summarized in Table 4.3.
Table 4.3 Simulation operating parameters; comparisons for varying pressure
Temperature (K)
Biomass feed load (%)
Residence time (s)
Feedstock
Pressure (MPa)
923
4
15-47
Wood sawdust + CMC
20-30
WGS reaction rate is favored by pressure due to the increase in SCW
water density. Reactions of the intermediates in solvent is linked to the diffusion
rate in water. Water may act like a physical barrier for reactions as a result of
64
high viscosity and reduce the rates. Increase in pressure therefore hinders these
type of reactions with the “cage effect” of high viscosity water.
CE and GE response to the pressure were found to be non-monotonic in
Lu’s experiments due to the property change effects of water on different
mechanisms. However, CE and GE trends with respect to the pressure are almost
linear in the simulation results. Difference between experimental and the
simulation results become smaller as pressure increases and at 30 MPa they are
in very good agreement. Deviation, especially below 25 MPa can be explained by
lack of the integration of the SCW property changes such as cage effect of high
viscosity water, to the reaction kinetics of the simulation. Convergence of the
results SCW region over 25 MPa (see Figure 4.13) approved the capability of the
kinetic study application by AspenPlusTM simulation at elevated pressures.
Efficiency (%)
120
100
80
CE
60
CE*
GE
40
GE*
20
18
20
22
24
26
Pressure (MPa)
28
30
32
Figure 4.13 Comparison between simulation results GE, CE and experimental results GE*, CE* for residence
reactor pressure between 200 and 300 bars
According to the product gas yield comparison shown in Figure 4.14, a
fair accordance was achieved over 25 MPa for carbon dioxide, hydrogen and
carbon monoxide; on the other hand, disagreement in methane enlarged with
pressure. Hydrogen, carbon dioxide and methane tend to increase with pressure
in the given range of pressure in a similar, linear behavior except carbon
monoxide, which decreases with pressure. Results of the study of Lu et al. [9]
show that product gas yield response is not monotonic but is affected by
pressure in contradictory ways due to the physical change in the solvent-solute
mixture, explained in the efficiency comparison section. Yield values with respect
to the pressure converges to the experiment values at 25 MPa and beyond.
65
Yield (mol gas/kg biodry)
22
H2
17
H2*
CO2
12
CO2*
7
CO
CO*
2
-3 18
20
22
24
26
Pressure (MPa)
28
30
CH4
32
CH4*
Figure 4.14 Comparison between product yield results of the simulation and experimental results (*), for
reactor pressure between 200 and 300 bars
4.3.3 Effect of Feedstock
The comparison of the gasification of various feedstock improves the
understanding of the quality of the simulation results for different structural
compound compositions. Experiments were run at the 650 ℃ maximum
temperature, 27 s residence time and 25 MPa operating pressure with a 4%
biomass loading for rice straw, peanut shell, corn stalk corn cob and wood
sawdust (2% CMC + 2% biomass). The composition data were obtained from
Phyllis biomass database [5]; by calculating the elemental composition from the
given biochemical composition and selecting the most similar samples with the
Lu’s feedstock. It should be noted that 2% CMC was summed with the biomass
cellulose content for the overall feed composition; furthermore, for the samples
with less than 100% biochemical and ash content remaining part was equally
added to the biochemical content. Resulting compositional mass flow rate of the
biomass feedstock (for 100 kg/hr feed flow) are shown in Table 4.4.
Table 4.4 Biochemical and ash mass compositions of dry biomass samples used in the simulation
Rice Straw
kg/hr
Peanut
Shell
kg/hr
Corn Stalk
Corn Cob
kg/hr
kg/hr
Wood
Sawdust
kg/hr
Cellulose
Hemicellulose
Lignin
Ash
2,79
0,50
0,31
0,40
2,77
0,44
0,67
0,12
2,91
0,67
0,37
0,06
3,04
0,64
0,30
0,02
2,99
0,47
0,53
0,01
Total
4
4
4
4
4
GE and CGE results show good accordance except for peanut shell and
corn stalk as shown in Figure 4.15. Corn cob simulation has the CE and GE values
almost identical with the Lu’s results [9]. The main reason for the different
66
results for corn stalk and wood sawdust are the use of different composition
samples. Lu et al. provide the elemental analysis of the samples, even though the
simulation is based on the chain decomposition reactions of cellulose,
hemicellulose and lignin model compounds by the mentioned kinetic approach.
Biochemical and ash analysis of the samples were collected from Phyllis biomass
database [5], resulted elemental composition of peanut shell and corn stalk are
different than from Lu’s samples, which is a common issue for biomass related
studies since their composition depends on many variables; soil, fertilizer,
weather condition and air quality. For set of the simulation runs, effect of the ash
content on reaction kinetics were neglected due to the negligible ash content in
wood sawdust; even though for rice straw, peanut shell and corn stalk
constitutes high ash content, which is believed to have catalyzing or inhibiting
effect on decomposition and gasification reactions in hydrothermal media.
120
Efficiency (%)
100
80
GE
60
GE*
CGE
40
CGE*
20
0
Rice Straw Peanut Shell Corn Stalk
Corn Cob
Wood
Sawdust
Figure 4.15 Comparison between simulation results GE, CE and experimental results GE*, CE* for different
feedstock; rice straw, peanut shell, corn stalk, corn cob and wood sawdust.
Comparison between Lu’s SCW biomass gasification work and the current
process simulation findings enabled the evaluation of the proposed model’s
strengths and weaknesses. First of all, the kinetic mechanism based SCWG
simulation enables to analyze the response of the system to the variation of
parameters, such as residence time, pressure and feed composition; not only for
the outlet conditions but also at a specific instant during the operation, as a
result of the kinetic approach. Overall gasification and carbon gas efficiency
results were in a very good agreement with the experimental results, both in
quantity and regarding qualitative response trend. This depicts the ability of
simulation to predict the degree of gasification of biomass feed, found as a
fruitful outcome for further process design works. Differences for low pressures
(𝑃 < 25 MPa) can be explained by kinetic studies done for 25 and 30 MPa, and
67
also with the lack of integration of pressure related inhibiting or promoting
effects of water (ion product, cage effect). Product gas yield comparison with the
experimental data is in a fair agreement. The differences between the simulation
results and Lu’s experimental results were in a reasonable range considering the
assumptions made on, organic acid types formed, direct gasification reaction
stoichiometry of lignin and derivatives and non-Arrhenius reactions. It should
also be noted that process conditions in the experimental work possibly scatters
from the ones set for simulation, e.g. residence time, heating trend biomass feed
load etc., which are suspected to lead to deviations. Simulation runs for different
types of biomass feedstock resulted in good agreement with experimental except
for the biomass samples with significantly different elemental compositions,
such as peanut shell and corn stalk. The approach of classifying main biomass
biochemicals and their reaction pathways improved the simulation precision for
various feedstocks with different biochemical compositions, which was also an
encouraging outcome of the current study.
68
5 Sensitivity Analysis
5.1
Sensitivity Analysis for Reactor Heating Rate Effect:
Biomass decomposition and gasification in sub- and supercritical water
occurs through numerous parallel and series reactions, in this study the
decomposition reactions of main biochemical compounds were the scope of
interest. Cellulose, hemicellulose and lignin goes into direct gasification or
decompose into the liquid intermediates, the latter path continues with
recombination or further decomposition to the gas products unless they are
stable enough to leave the set of reactor unconverted. High gasification efficiency
pathway for each biochemical compound exists if the suppression of tar, char
and stable intermediates is achieved. Tar and char formation minimization are
the main challenges of biomass gasification systems. Tar barely gasifies when it
is formed, which leads to the plugging and loss of efficiency and is mainly
sourced by molecular furfural and similar structured polymers. In addition to tar
and char, formation of slow decomposing intermediates lower gasification
efficiency, for a given residence time. Biomass constituents mainly convert
through radical and ionic mechanisms. As described in the Chapter 2, the former
follows the Arrhenius behavior and reaction rate increases with temperature
regardless of the physical condition of water. Ionic reactions are affected by the
dielectric constant and ion product of water. Ionic reactions occur at high rate
when the water has a high ion product, which is the case in the near critical
region. Evaluation of heating rate effect is vital in order to develop the reactor
and process conditions for a high gaseous throughput without reactor plugging
problems. Organic acids and derivatives were detected to be rather stable
compounds, requiring longer residence times even in supercritical water.
This part of the work aims to simulate the complete reaction network
with kinetic approach, which allows gathering information at a specific time and
length of the tubular reactor. Even though reduction of the rates with
temperature in supercritical water was not applicable due to the power law type
reaction set of AspenPlusTM, keeping the rate constants of the ionic reactions at
their average value while others increase with temperature in supercritical
water, helps to understand advantageous temperature profile for the outlet
condition. Furthermore, Arrhenius parameters of the radical type reactions
determine the rate constant dependence to the temperature, which can only be
evaluated by kinetics approach followed in this set of work.
In order to observe the changes in gasification efficiency, product gas
yield and amount of char formed according to the temperature trend inside the
tubular reactor, three different scenarios were simulated. For the sensitivity
analysis the highest temperature of the sub- and supercritical reactor was
designed to reach at its half-length instead of a sharp increase in the beginning
and short residence time is given for the reactions; the former approach was
applied to generate meaningful results for the higher capacity applications
(higher heating requirements), the latter approach was followed to evaluate the
efficiency results better at moderate values and. Corn cob was chosen as
feedstock sample according to the promising results obtained in the validation
69
section. Inlet conditions were selected as 100 kg/hr feed flow rate 12% biomass
load at 25 oC and 25 MPa. Simulation set parameters are presented in Table 5.1.
Table 5.1 Simulation set parameters
Feed flow rate
Biomass type
Dry bio load
Biochemical & Ash
flow rate
Cellobiose
Xylose
Guaiacol
Ash
Reactor dimensions
Subcritical Reactor
Supercritical Reactor
kg/hr
Diameter
Length
Max. Temp.
Diameter
Length
Max. Temp.
wt. %
100
Corn Cob
12
kg/hr
kg/hr
kg/hr
kg/hr
9,12
1,92
0,90
0,06
m
m
oC
m
m
oC
0,014
1,40
370
0,05
4,60
650
The first scenario was simulated as a linearly increasing temperature
profile in the whole tube length and preheating in order to reach the maximum
temperature halfway the tube length followed by isothermal operation in the
other half for subcritical and supercritical reactors, respectively (Figure 5.1 and
Figure 5.2).
70
0
1
Residence time (s)
3
4
2
5
6
7
400
350
300
250
200
150
100
50
0
L vs T
t vs T
0
0.5
1
1.5
Tube Length (m)
Figure 5.1 Temperature profile in subcritical reactor for the 1st scenario
0
5
10
Residence time (s)
15
20
25
30
700
600
500
400
L vs T
300
t vs T
200
100
0
0
1
2
3
4
5
Length (m)
Figure 5.2 Temperature profile in supercritical reactor for the 1st scenario
The second scenario involved simulation for both of the reactors as;
preheating in order to reach the maximum temperature at the halfway followed
by isothermal operation in the other half (Figure 5.3 and Figure 5.4).
71
0
Residence time (s)
2
3
1
4
5
400
350
300
250
200
150
100
50
0
L vs T
t vs T
0
0.5
1
1.5
Tube Length (m)
Figure 5.3 Temperature profile in subcritical reactor for the 2nd scenario
0
Residence time (s)
10
15
20
5
25
30
700
600
500
400
L vs T
300
t vs T
200
100
0
0
1
2
3
4
5
Length (m)
Figure 5.4 Temperature profile in supercritical reactor for the 2nd scenario
The third scenario was simulated as; preheating in order to reach the
maximum temperature at the halfway followed by isothermal operation in the
other half and linear increasing temperature profile in the whole tube length for
subcritical and supercritical reactors, respectively (Figure 5.5 and Figure 5.6).
72
0
Residence time (s)
2
3
1
4
5
400
350
300
250
200
150
100
50
0
L vs T
t vs T
0
0.5
1
1.5
Tube Length (m)
Figure 5.5 Temperature profile in subcritical reactor for the 3rd scenario
0
5
10
Residence time (s)
15
20
25
30
35
700
600
500
400
L vs T
300
t vs T
200
100
0
0
1
2
3
4
5
Length (m)
Figure 5.6 Temperature profile in supercritical reactor for the 3rd scenario
Gasification efficiency and carbon efficiency results for different reactor
temperature trends should be evaluated with the knowledge of reaction
mechanisms of cellulose, hemicellulose and lignin decomposition. Glucose and
fructose are the main hydrolysis product of cellulose, which convert further into
aldehydes, ketones, and acids or to the aromatics such as furfural and 5HMF[53]. The mechanism of the former path is mostly radical which follows the
Arrhenius behavior in both sub- and supercritical water, while the ionic
reactions take place mostly at the near critical water due to the high
concentration of H3O+ and OH- ions (catalytic effect). Produced aromatics in
subcritical region are more stable and prone to form higher molecular polymers
that cannot (easily) be gasified. On the contrary, organic acids formed in the
subcritical region are degradable to the product gases in the high temperature
supercritical region and this results in a relatively high gasification efficiency [9].
The highest gasification and carbon efficiency is obtained in the first scenario,
which has low average temperature in the subcritical reactor and high average
temperature in the supercritical reactor (preheating in the first half). Results (in
73
Figure 5.7) agree with the kinetic study findings; the temperature profile of the
first scenario suppresses the 5-HMF and furfural formation in the subcritical
reactor and favors the radical type gasification reactions in the supercritical
reactor, which result in high gasification and carbon efficiencies. The lowest
efficiencies obtained in the third scenario shows the importance of the
gasification reactions that take place in the high temperature supercritical
region, since most of the radical type liquid to gas reactions have an elevated
activation energy barrier.
90
84.27
82.19
80
66.97
Efficiency (%)
70
64.98
59.10
60
49.57
50
40
30
20
10
0
GE
CE
1st
2nd
3rd
Figure 5.7 GE and CE values of 1st 2nd and 3rd scenarios
The percentage of carbon in unconverted liquid effluent indicates the
degree of formation of rather stable products; furthermore, it is a result of short
time spent in the maximum temperature region. An inverse relation between
gasification efficiency and unconverted liquid holds for both analytic and kinetic
reasons; hence the results can be justified with the same reasons mentioned
above.
Char formation from biomass in hydrothermal gasification is caused
mainly by conversion of lignin and derivatives [61]. Char from guaiacol and its
derivatives is formed by crosslinking of the active sites and creation of high
molecular weight structures which is favored in the supercritical region at high
temperatures. Carbon molar flow by means of char was calculated by subtracting
the carbon molar flow rates of intermediate liquid compounds and gaseous
products from carbon in biomass feed. As can be seen in Figure 5.8, the first and
second scenario result in higher chars formation, which agrees with the kinetic
findings. Carbonunc. liq (%) refers to the molar ratio of carbon contained in the
74
liquid effluent to the carbon fed with the biomass. NCarbon (char) is the molar
carbon flow in the outlet (sub stream), which was calculated by subtracting the
number of moles of carbon in gas and liquid from biomass.
1st
2nd
3rd
45
14
40
12
35
25
8
20
6
15
Carbon char (%)
Carbon unc. liq. (% )
10
30
4
10
2
5
0
0
1st
2nd
3rd
Figure 5.8 Carbon mole ratio of unconverted liquid in the effluent, Carbon (unc.liq)% and molar flowrate of
carbon contained in char for 1st 2nd and 3rd scenarios
In Figure 5.9, product gas composition results from the multiple scenarios
are shown. High gas yields in the first two scenarios are linked with gasification
efficiency results. It should be noted that Carbon efficiency results represent the
abundance of carbon containing gas in the product. Higher composition of
carbon monoxide in the third scenario can be explained by the presence and
importance of WGS reaction. Supercritical region and high temperature favors
the WGS reaction, hence its effect is more significant in the first two scenarios.
75
100%
90%
Composition (%)
80%
70%
60%
50%
CH4
40%
CO
30%
CO2
20%
H2
10%
0%
1st
2nd
3rd
CH4
7.9
8.3
22.1
CO
19.5
18.8
15.5
CO2
34.9
34.9
24.8
H2
37.6
38.0
37.7
Figure 5.9 Product gas composition values for first, second and third scenarios
76
5.2
SCWG Process Scheme Build-Up & Sensitivity Analysis for
Parameter Variations on SCWG Plant Simulation
Plant design and simulation of the SCWG process was done in order to
analyze the combustible gas production rate, efficiency and self-sufficiency of the
system with more realistic feed rate and also to create simple process flowsheet
including auxiliary units. By doing so, biomass to product gas and liquid effluent
pathway was completed; additionally, the influence of various process variables
such as maximum operating temperature, biomass load in the feed and type of
biomass (effect of biochemical composition) were quantified.
5.2.1 Design Parameters and Unit Selections
For the overall process scheme build up, early found favorable process
conditions were applied with the literature examples [43], [67]. Main process
units used (with the aid of AspenPlusTM) were mixer, tubular flow reactor, liquidliquid separator, vapor-liquid separator, water pump and pressure relief valves.
The basic plant scheme is shown in the Annex-D. The feed stream was pumped to
the operating pressure and fed to the first reactor. It should be noted that,
tubular flow reactors are at the same time heat-exchanging units, which enables
to compute the reaction rates and intermediate evolution during the heat-up
phase similar to the real application. The reactor system is separated into three
parts according to the temperature levels; in the first reactor the stream is
heated up to 300 ℃ (Preheat), in the second reactor the critical condition is
almost reached with an outlet temperature of 370 ℃ (Superheat) and the third
reactor is where supercritical phenomena happen from 374℃ to the maximum
temperature (550 – 650 ℃). The outlet stream from the 3rd reactor (Heatrcy1) is
a mixture of supercritical water, intermediate liquid compounds and product
gases. Heatrcy1 stream is recycled for in-direct thermal heat exchange with the
reacting stream. Routlet1 stream leaving the 1st tubular reactor after the heat
rejection section is first cooled down to 35 ℃ (by a fresh cold water feed possibly
from a river or possible design of air-water cooling systems) and fed the liquid
separator unit (Separ), where high molecular weight insoluble compounds or
polymers are separated from the gas-rich pressurized water stream. In the
current study, all ungasified hydrocarbons were separated aiming to improve
vapor-liquid equilibrium calculation performance of the simulation, which is
prone to be disrupted by the presence of complex hydrocarbons.
Water gas mixture (H2O+gas) then flows to the high-pressure vapor-liquid flash
separator, where product gas rich in H2 is separated from liquid down flow. The
liquid stream is then de-pressurized to atmospheric conditions and enters to the
low-pressure vapor-liquid flash separator. In this column, most of the CO2 solved
in high-pressure water is separated from water with other remaining product
gases. Product gas streams can be classified as, H2 rich high-pressure gas
(PRODGAS1), CO2 rich low pressure gas (PRODGAS2). For commercial
applications, the high pressure stream can be upgraded further for fuel cell uses,
while low pressure stream can be utilized to produce extra heat to be used
internally by the supercritical reactor if found economically viable. Unconverted
organics and part of water can be recycled to the feed; in the current work mass
77
recycling was not considered due to very low concentrations of the unconverted
organic compounds, which can be a further development on the subject
especially for the biomass types containing higher lipid or protein, such as algae.
Component molar flows in key process streams listed in Annex-D.
Heat Transfer Analysis
Heat exchange between recovering stream (Heatrcy1) and reacting flow is the main drive
for the self-sufficiency of high-energy demand process. Design and scaling of the heat exchangers
(in-direct flow passing in tubular reactors) with the most accurate results is believed to be possible
with the differential approach to the heat transfer between rapid property changing pseudo
liquid/vapor phase fluids and also the heat of reactions with the simultaneous moles of the organic
compounds. With the consideration of the main object of the work; integration, monitoring and
analysis of the kinetic model, a rather simplified method was applied for the heat exchanger design;
by applying a constant overall heat transfer coefficient value, in accordance with the built in
“counter-cool” heat exchangers of the software. Design temperature estimations or calculations of
the heat exchange (between the thermal fluids) type tubular reactors are still not available in the
literature owing to the novelty of the process, except for simplified process schemes of the pilot
plant designs, please see paragraph 1.5 [7]. By making a subjective assumption based on the reactor
sizing conditions and density fluctuations and with the associated tubular flow reactor scheme [7], a
150 ℃ difference between the outlet temperature of the gasifier was selected. Inside the horizontal
heat exchanger, no significant pressure loss was assumed, and the hot fluid outer tube, as well as the
cold fluid inner tube design was specified. Heat transfer calculation results are directly linked with
the reactor tube sizings (
Table 5.2).
The section of the heat exchange process is shown in Figure 5.10. Average
convective heat transfer coefficients of the hot stream (hhot @255 ℃ < 𝑇ℎ𝑜𝑡 <
650 ℃), cold stream (hcold @ 25 ℃ < 𝑇𝑐𝑜𝑙𝑑 < 500 ℃) and conductive heat
transfer coefficient of steel tube (kwall) are 60,000 W/m2. K, 20,000 W/m2. K, 19
W/m.K, respectively [68].
Figure 5.10 Simplified heat exchange unit scheme
78
Log-mean temperature difference (∆𝑻𝑳𝑴 ) was achieved within given temperature range for the
counter-current heat exchanger (Equation 5-1). The overall heat transfer coefficient (𝑼) was
calculated by using convective and conductive heat transfer resistances, as expressed by Equation
5-2. A typical residence time-reactor length was known for a hydrothermal gasification process;
hence, inner and outer diameters of the tube inside were calculated (thickness was checked to be inline with the security limitations), as shown in
Table 5.2. The heat transfer rate was calculated from heat duty of the water
heating between 500 and 650 ℃ and 25 MPa with the aid of AspenPlusTM
simulation, to be consistent.
∆𝑇𝐿𝑀 =
(𝑇𝐻,𝑖𝑛 − 𝑇𝐶,𝑜𝑢𝑡 ) − (𝑇𝐻,𝑜𝑢𝑡 − 𝑇𝐶,𝑖𝑛 )
(𝑇
− 𝑇𝐶,𝑜𝑢𝑡 )
ln ( 𝐻,𝑖𝑛
⁄
)
(𝑇𝐻,𝑜𝑢𝑡 − 𝑇𝐶,𝑖𝑛 )
𝑈=
1
𝑟 . ln(𝑟𝑜 − 𝑟𝑖 )
1
1
+ 𝑖
+
ℎ𝑐𝑜𝑙𝑑
𝑘𝑤𝑎𝑙𝑙
ℎℎ𝑜𝑡
𝑄 = 𝑈. (𝜋. 𝐷𝑡𝑢𝑏𝑒 . 𝐿𝑡𝑢𝑏𝑒 ). ∆𝑇𝐿𝑀
Equation 5-1
Equation 5-2
Equation 5-3
Table 5.2 Tubular reactor sizing parameters
Transfer
parameters
∆𝑇𝐿𝑀
ℎ𝑐𝑜𝑙𝑑
ℎℎ𝑜𝑡
𝑘𝑤𝑎𝑙𝑙
𝑈
(K)
(W/m2.K)
(W/m2.K)
(W/m.K)
(W/m2.K)
187
20.000
60.000
19
1090
douter(mm)
12
12
26,7
dinner(mm)
8
8
15,6
Reactors
1st
Reactor
2nd Reactor
3rd Reactor
length (m)
22,5
16
22
Residencetime (s)
7,2
3,3
22,9
79
Gasification and Carbon efficiency definitions are shown in the validation
paragraph 4.3 Cold gas efficiency calculations (CGE) were made by dividing
thermal energy achieved from product gas to the thermal energy of the biomass
load (by achieving hypothetically the full recovery from the lower heating
values) and externally supplied heat duty (see Equation 5-4).
𝐶𝐺𝐸 =
∑𝑖 𝑚𝑔𝑎𝑠,𝑖 . 𝐿𝐻𝑉𝑖
∙ 100%
𝑚𝑏𝑖𝑜,𝑑𝑟𝑦 . 𝐿𝐻𝑉𝑏𝑖𝑜,𝑑𝑟𝑦 + 𝑄𝑠𝑢𝑝𝑝,𝑡ℎ
Equation 5-4
Where 𝐿𝐻𝑉 refers to the lower heating value and (MJ/kg) and 𝑚 refers to the
mass flowrate (kg/s).
5.2.2 SCWG of Cellulosic Biomass Plant Process Simulation
For the base case simulation, Corn cob was selected as a feed sample
based on the comparatively good agreement in the validation chapter. Biomass
mass concentration in feed was chosen to be 15%, since it was found as a minim
load for thermally self-sufficient operation in another simulation study using the
Gibbs free energy minimization approach [42], demanded to be further analyzed
by a kinetic approach. Reactor pressure was selected to be 25 MPa for all runs in
3 consecutive reactors (for SCWG process scheme please see Annex-D).
Maximum temperature of 650 ℃ was applied for the base simulation, due to its
promising gasification efficiency results in the previous chapter. The mixer is fed
with water biomass mixture at 25 ℃ and 1 bar with 500 L/hr flow total rate.
Recovered stream after final cooling to the 35 ℃, is separated from ungasified
liquid compounds, and then gas-liquid separation was done by high and low
pressure flash separators. Results were calculated from the molar flow rate
values of the associated streams by using Equation 4-7, Equation 4-8, Equation 4-9 and
Equation 5-4.
Table 5.3 Base case simulation results
Simulation (Base Case)
Efficiency Values (%)
GE
CE
CGE
Gas Yield (molgas,i/kgbiomass,dry)
H2
CO2
CH4
CO
Gas Composition (molgas,i/molgas,tot %)
H2
CO2
CH4
92,2
81,6
59,3
17,1
16,1
8
1,9
39,7
37,3
18,5
80
CO
4,5
5.2.3 Effect of Maximum Temperature
Efficiency (%)
Gas phase and intermediate to gas reaction rates are favored by high
temperature in the supercritical region. External heating demand, on the other
hand, increases with temperature of the supercritical reactor, which would
negatively impact the thermal efficiency. The degree of change of GE and CE at
the third reactor outlet temperature (Tmax) will therefore be beneficial for
process parameter decision-making. Sensitivity analysis for Tmax was run for 3
cases, 650 ℃, 600 ℃ and 550 ℃. Resulting GE and CE values are presented in
Figure 5.11.
100.00
90.00
80.00
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
92.24
81.59
76.41
70.37
58.70
56.98
GE
CE
650 ℃
600 ℃
550 ℃
Figure 5.11 GE and CE results of the process simulation for Tmax= 650℃, 600 ℃, 550 ℃
GE and CE values increase with Tmax, a natural result of high activation
energy requiring gasification reactions, which are mostly stagnant at low
temperatures. Product gas formation shows a monotonic relation with Tmax,
although optimum level can be understood with the further heat transfer
analysis.
81
5.2.4 Effect of Biomass Load
Hydrothermal biomass conversion processes generally suffer from only
allowing low biomass feed shares in the water. In this set of analysis four
different cases were evaluated according to the biomass load, 10, 12, 15, 18 and
20 %. Aim of the set of runs was to analyze the best load value in terms of
gasification and thermal efficiency of the process. Figure 5.12 illustrates the
response of GE, CE and GCE to the load variation. Figure 5.13 and Figure 5.14
show supplied external thermal energy (Qsupp) and net thermal output (Qth, net);
specific supplied external thermal energy (Qsupp, spec) and specific net thermal
output (Qth, net, specific) responses to the biomass load, respectively. Qth, net
calculation was done according to Equation 5-5.
Qth, net = ∑ 𝑚𝑔𝑎𝑠,𝑖 . 𝐿𝐻𝑉𝑖 − Q𝑠𝑢𝑝𝑝,𝑡ℎ
Equation 5-5
𝑖
𝑄 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 =
GE
Qth, net
𝑚𝑏𝑖𝑜,𝑑𝑟𝑦
CE
Equation 5-6
CGE
100
Efficiency (%)
90
80
70
60
50
40
8
10
12
14
16
Biomass Load (%)
18
20
22
Figure 5.12 Biomass load effect on GE and CE
82
According to Figure 5.12, GE between 10 to 15 % of biomass load does
not change significantly; at 18% of load it increases from 92% to 96% then
decreases back to the same level at 20%. GE mainly defines how efficiently the
biomass is converted into gaseous product. Results obtained show that if
reaction kinetics and reactor is designed to prevent clogging issues suffered
mostly with the dense streams, biomass load as high as 18% would result in the
most efficient biomass to gas conversion. The difference between GE and CE
results from the H2 composition in the product gas stream, in other words, CE is
a measure of the efficiency of the Carbon containing gases (e.g. CO2, CH4), which
has its optimum value at 15%, meaning the highest biomass to gas carbon
conversion at that point is expected to be realized. CGE trend illustrates that with
high load thermal efficiency of the system is favored even though the mass flow
of the dry biomass feed (denominator of the CGE definition) increases; which
points out favoring effect of concentration on SCWG. For CGE, 18% load is the
optimum value beyond which a decrease in CGE is observed. Definite kinetic
explanation of the resulted responses is trivial to interpret due to high number
and complexity of the reaction network. However, a significant effect of
concentration on decomposition reaction rates can be seen from the results
obtained.
Qsupplied
Qth, net
0.3
0.25
Q (MW)
0.2
0.15
0.1
0.05
0
8
10
12
14
16
Biomass Load (%)
18
20
22
Figure 5.13 Biomass load effect on thermal energy supplied and net thermal energy production rate
As illustrated in Figure 5.13, net thermal output (Qth, net) of the SCWG
process increases, while supplied thermal energy for heating up the fluid (Qsupp.)
stays constant with biomass load. The horizontal trend of Qsupp. vs biomass load
line is a result of water like behavior of the stream with dilute biomass content,
hence unchanged heat transfer parameters. High water content in the stream
absorbs to a large extent the energy supplied, and leaves the heating demand of
biomass insignificant. According to the results obtained, energy production from
SCWG process is favored by high biomass load as long as clogging due to high
load is prevented.
83
Qsupplied,specific
Qth, net, specific
10
9
Q specific (MJ/kg)
8
7
6
5
4
3
2
1
0
8
10
12
14
16
Biomass Load (%)
18
20
22
Figure 5.14 Biomass load effect on specific thermal energy supplied and net thermal energy production
rate
As can be viewed in Figure 5.14 Specific thermal output increases with
load from 10 to 18% where it reaches a maximum. A similar trend of the Qth, net,
specific and GE depicts the consistency of the sensitivity analysis since both
parameters evaluate the biomass to gas conversion performance of the system,
for unit feed. Qsupplied, spec decreases with biomass load, simply explained by
almost constant numerator (Qsupplied) and increasing denominator (biomass feed
rate).
5.2.5 Effect of Biomass Type
The reaction kinetic approach of the current work enables to analyze the
compositional effect of biochemical constituents in real biomass. Reaction
network and kinetic parameters of cellulose (cellobiose), hemicellulose (xylose)
and lignin (guaiacol) are significantly different, resulting in gasification rate and
outlet composition variances. Comparison between SCWG of corn cob and rice
husk will enable one to evaluate the influence of biomass constituents on process
performance. Rice husk-comes up with high moisture content owing to the
condition of the crop land- is a good example of high moisture biomass, a
possible target of the real application. Corn cob/Rice straw has cellulose,
hemicellulose, lignin and ash content of 52/39,3, 32/25, 15/15,9 and 1/19,8 %,
respectively (see Table 4.4) . Significantly high ash of rice straw results in low
biochemical content. In reaction sets ash was accepted as non-reacting inorganic,
84
and possible catalytic or inhibiting effects were disregarded, due to the variance
in ash compositions and deficient data in the literature. Furthermore, lignin
composition (excluding ash) of rice straw is notably higher than corn cob.
Process operating parameters are set to the base case as can be viewed in Table
5.1.
Figure 5.15 and Figure 5.16 present the GE, CE, CGE and product gas
compositions for each biomass type, respectively.
100.00
92.24
90.00
81.59
78.82
Efficiency (%)
80.00
68.89
70.00
53.62
60.00
47.55
50.00
40.00
30.00
20.00
10.00
0.00
GE
CE
Corn Cob
CGE
Rice Husk
Figure 5.15 Biomass type effect on GE, CE and CGE
Figure 5.15 shows that, GE and CE values of corn cob are roughly 10 %
higher than the ones of rice husk. Poor biomass to gas conversion efficiency of
rice husk is primarily linked to the high non-reacting ash content. Dominating
effect of ash content theory is supported by the comparison of carbon fraction of
the liquid intermediate and biomass feed. Figure 5.15 displays that more carbon
conversion is achieved from biomass to gas, even though inert ash dominates the
efficiency results. CGE results follow the trends of the former ones, since poor
gasification efficiency results in low combustible gas production with almost the
same reactor heat duties.
85
=
120
Gas Composition (%)
100
4.45
4.17
18.5
19.4
37.3
37.8
39.7
38.7
80
60
40
20
0
Corn Cob
Rice Husk
H2
CO2
CH4
CO
Figure 5.16 Biomass type effect on product gas compositions
CO2, CH4, and CO compositions are higher for rice husk, whilst lower for
H2 as depicted in Figure 5.16. The effect of the biochemical load to the product
gas composition is an expected and fruitful outcome of the kinetic approach.
Reaction network of cellulose is prone to end up in high H2 content product gas;
on the other hand, lignin produces more carbon containing gases.
86
Conclusions and Further Developments
This work has addressed the following questions:
Q1. Are the reported experimental kinetic data for individual model biomass
compounds capable of predicting the real lignocellulosic biomass (mainly
agricultural residues) behavior?
SCWG of biomass, reaction kinetic model and pathway was developed
according to the experimental findings in the literature for main biomass
constituents; cellulose, hemicellulose and lignin. Current data pool in the
literature was found to be sufficient to propose a network for the whole reacting
system, even though precision of the model can always be improved with
possible future findings especially about; cellulose decomposition reactions in
supercritical water over 400 ℃ (with detailed compositional data of
intermediates), organic acid types formed from glucose-fructose derivatives,
equation stoichiometry of lignin and derivatives gasification reactions, 5-HMF
decomposition reactions in supercritical water. Furthermore, reaction rate
equations that include H3O+ and OH- ions will enable to tune water properties to
minimize aromatics and char forming dehydration and polymerization reactions
in sub- and near-critical water. Precision of the composition calculations can be
further improved with the addition of nth order reactions for required cases.
Q2 AspenPlusTM software capable of simulating the SCWG of model
compound combinations and deliver valid results based on the reaction
kinetics approach?
Validation test results showed that detailed research on kinetic data in
the literature pays off with realistic process simulation outcomes. Simulation
was capable of running not only with model compounds, but also with real
biomass as a combination of cellulose, hemicellulose and lignin. Gasification and
carbon efficiency results were promising with relative error range of 13% and
8% for residence times between 15-47 seconds. Pressure variation effect on
gasification efficiency (GE) and carbon efficiency (CE) showed agreement with
the experimental findings for pressures of 25 MPa and 30 MPa. Product gas yield
results were promising due to the similar response to the residence time and
pressure modifications except CH4, which was off range. SCWG simulation was
found successful for giving precise results for various biomass types, such as rice
straw, corn cob and wood sawdust. Char and tar formation sourced by glucose
and fructose as well as other ionic mechanisms can be modeled more precisely
by implementing rate constant - water dielectric constant ion product relation.
Fluidized bed simulation with the updated version of AspenPlusTM can be the
87
further study to examine the influence of high mass-heat transfer as well as
catalyst bedding material.
Q3. What can be the possible process conditions to favor high gas and low
tar/char yields?
Sensitivity analysis results pointed out that SCWG of biomass efficiency
parameters, product gas yields/compositions and tar/char formation rates were
strongly influenced by process conditions, such as temperature profile in the
reactors, maximum temperature reached, biomass concentration in feed and
type of the feed. The highest GE and CE results were attained for rapid heating to
650 ℃ in the supercritical reactor (compared to a linear temperature profile
inside the supercritical reactor), 18% biomass load with corn cob. Highest char
formation was observed for the case where subcritical and supercritical reactors
are both preheated, linked to favoring the aromatics to char reactions at elevated
temperatures.
Q4. Is biomass gasification based on the SCWG process feasible with
reasonable yields and thermal self-sufficiency?
According to the SCWG process simulation for 650 ℃ maximum
temperature, reactor pressure, 500 L/hr feed rate with 15% biomass (corn cob)
concentration 184,4 kW of net thermal energy output (Qth,net) was achieved (by
assuming the exploitation of product gas LHV with full efficiency), hence selfsustainability of system was depicted. Qth,net increase was observed with higher
biomass content, at 20% biomass load the maximum energy production rate of
263,8 kW was attained; it should be noted that, plugging issues related with high
biomass load should be studied experimentally. It was also found that high ash
and lignin content in biomass has inhibiting effect on gasification efficiency and
energy output of the process.
88
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94
Annexes
A- Subcritical Region Reactions
Reaction
Rxn No. type
1
Kinetic
2
Kinetic
3
Kinetic
ge.g
Kinetic
gg.g
Kinetic
g.f
Kinetic
g.e
Kinetic
g.a
Kinetic
g.gly
Kinetic
f.e
Kinetic
f.gly
Kinetic
gly.dih
Kinetic
dih.gly
Kinetic
gly.p
Kinetic
dih.p
Kinetic
f.acid
Kinetic
p.acid
Kinetic
a.acid
Kinetic
e.acid
Kinetic
glyo.acid Kinetic
fa.ga1
Kinetic
g.5
Kinetic
f.5
Kinetic
5.lf
Kinetic
5.ff
Kinetic
xy.fu
Kinetic
xy.gm
Kinetic
fu.aa
Kinetic
gu.t
Kinetic
gu.b
Kinetic
gu.c
gu.oc
c.t
Kinetic
Kinetic
Kinetic
c.oc
Kinetic
Stoichiometry
CELLOBIO(MIXED) --> GLYCOERY(MIXED) + GLYCOLAL(MIXED)
CELLOBIO(MIXED) --> GLYCOGLY(MIXED) + ERYTHROS(MIXED)
CELLOBIO(MIXED) + WATER(MIXED) --> 2 DEXTR-01(MIXED)
GLYCOERY(MIXED) + WATER(MIXED) --> DEXTR-01(MIXED) + ERYTHROS(MIXED)
GLYCOGLY(MIXED) + WATER(MIXED) --> DEXTR-01(MIXED) + GLYCOLAL(MIXED)
DEXTR-01(MIXED) --> D-FRU-01(MIXED)
DEXTR-01(MIXED) --> 1.5 ERYTHROS(MIXED)
DEXTR-01(MIXED) --> LEVOG-01(MIXED) + WATER(MIXED)
DEXTR-01(MIXED) --> 2 GLYCERAL(MIXED)
D-FRU-01(MIXED) --> 1.5 ERYTHROS(MIXED)
D-FRU-01(MIXED) --> 2 GLYCERAL(MIXED)
GLYCERAL(MIXED) --> DIHYDROX(MIXED)
DIHYDROX(MIXED) --> GLYCERAL(MIXED)
GLYCERAL(MIXED) --> PYRUV-01(MIXED) + WATER(MIXED)
DIHYDROX(MIXED) --> PYRUV-01(MIXED) + WATER(MIXED)
D-FRU-01(MIXED) --> 2 LACTI-01(MIXED)
PYRUV-01(MIXED) + WATER(MIXED) --> LACTI-01(MIXED)
LEVOG-01(MIXED) + WATER(MIXED) --> 3 ACETI-01(MIXED)
ERYTHROS(MIXED) --> 2 ACETI-01(MIXED)
GLYCOLAL(MIXED) --> ACETI-01(MIXED)
FORMI-01(MIXED) --> CARBO-01(MIXED) + HYDRO-01(MIXED)
DEXTR-01(MIXED) --> 5-HYD-01(MIXED) + 3 WATER(MIXED)
D-FRU-01(MIXED) --> 5-HYD-01(MIXED) + 3 WATER(MIXED)
5-HYD-01(MIXED) + 2 WATER(MIXED) --> LEVUL-01(MIXED) + FORMI-01(MIXED)
5-HYD-01(MIXED) --> FURFU-01(MIXED) + FORMA-01(MIXED)
D-XYL-01(MIXED) --> FURFU-01(MIXED) + 3 WATER(MIXED)
D-XYL-01(MIXED) --> GLYCERAL(MIXED) + METHYLFO(MIXED)
FURFU-01(MIXED) + 2 WATER(MIXED) --> ACETI-01(MIXED) + ACRYL-01(MIXED)
1.7143 GUAIA-01(MIXED) --> BIPHE-01(MIXED) + 1.857 WATER(MIXED) + 0.786
OXYGE-01(MIXED)
0.8571 GUAIA-01(MIXED) --> BENZE-01(MIXED) + 0.4286 WATER(MIXED) + 0.6429
OXYGE-01(MIXED)
0.8571 GUAIA-01(MIXED) + 0.3571 OXYGE-01(MIXED) --> CATECHOL(MIXED) +
0.4286 WATER(MIXED)
GUAIA-01(MIXED) --> O-CRE-01(MIXED) + 0.5 OXYGE-01(MIXED)
2 CATECHOL(MIXED) --> BIPHE-01(MIXED) + 1.5 OXYGE-01(MIXED) + WATER(MIXED)
1.16667 CATECHOL(MIXED) + 0.5 WATER(MIXED) --> O-CRE-01(MIXED) + 0.916667
OXYGE-01(MIXED)
95
t.ph
Kinetic
oc.t
t.b
b.ch
la.acry
acry.la
la.acet
acet.aa
acry.hpa
hpa.acry
acry.pa
hpa.glyco
fa.ga2
l.la
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
BIPHE-01(MIXED) + 0.5 OXYGE-01(MIXED) + WATER(MIXED)
--> 2 PHENO01(MIXED)
1.714 O-CRE-01(MIXED) + 0.0716 OXYGE-01(MIXED) --> BIPHE-01(MIXED) + 1.857
WATER(MIXED)
BIPHE-01(MIXED) + WATER(MIXED) --> 2 BENZE-01(MIXED) + 0.5 OXYGE-01(MIXED)
BENZE-01(MIXED) --> CHAR(NC)
LACTI-01(MIXED) --> ACRYL-01(MIXED) + WATER(MIXED)
ACRYL-01(MIXED) + WATER(MIXED) --> LACTI-01(MIXED)
LACTI-01(MIXED) --> ACETA-01(MIXED) + CARBO-02(MIXED) + WATER(MIXED)
ACETA-01(MIXED) + WATER(MIXED) --> ACETI-01(MIXED) + HYDRO-01(MIXED)
ACRYL-01(MIXED) + WATER(MIXED) --> 3HPA(MIXED)
3HPA(MIXED) --> ACRYL-01(MIXED) + WATER(MIXED)
ACRYL-01(MIXED) + WATER(MIXED) --> PROPI-01(MIXED) + 0.5 OXYGE-01(MIXED)
3HPA(MIXED) + 0.75 OXYGE-01(MIXED) --> 1.5 GLYCO-01(MIXED)
FORMI-01(MIXED) --> CARBO-02(MIXED) + WATER(MIXED)
LEVUL-01(MIXED) + WATER(MIXED) --> LACTI-01(MIXED) + ACETA-01(MIXED)
B- Supercritical Region Reactions
Rxn No.
1
2
3
ge.g
gg.g
g.f
g.e
g.a
g.gly
f.e
f.gly
gly.dih
dih.gly
gly.p
dih.p
f.acid
p.acid
a.acid
e.acid
glyo.acid
g.5
f.5
5.lf
g.ff
Reaction
type
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Stoichiometry
CELLOBIO(MIXED) --> GLYCOERY(MIXED) + GLYCOLAL(MIXED)
CELLOBIO(MIXED) --> GLYCOGLY(MIXED) + ERYTHROS(MIXED)
CELLOBIO(MIXED) + WATER(MIXED) --> 2 DEXTR-01(MIXED)
GLYCOERY(MIXED) + WATER(MIXED) --> DEXTR-01(MIXED) + ERYTHROS(MIXED)
GLYCOGLY(MIXED) + WATER(MIXED) --> DEXTR-01(MIXED) + GLYCOLAL(MIXED)
DEXTR-01(MIXED) --> D-FRU-01(MIXED)
DEXTR-01(MIXED) --> 1.5 ERYTHROS(MIXED)
DEXTR-01(MIXED) --> LEVOG-01(MIXED) + WATER(MIXED)
DEXTR-01(MIXED) --> 2 GLYCERAL(MIXED)
D-FRU-01(MIXED) --> 1.5 ERYTHROS(MIXED)
D-FRU-01(MIXED) --> 2 GLYCERAL(MIXED)
GLYCERAL(MIXED) --> DIHYDROX(MIXED)
DIHYDROX(MIXED) --> GLYCERAL(MIXED)
GLYCERAL(MIXED) --> PYRUV-01(MIXED) + WATER(MIXED)
DIHYDROX(MIXED) --> PYRUV-01(MIXED) + WATER(MIXED)
D-FRU-01(MIXED) --> 2 LACTI-01(MIXED)
PYRUV-01(MIXED) + WATER(MIXED) --> LACTI-01(MIXED)
LEVOG-01(MIXED) + WATER(MIXED) --> 3 ACETI-01(MIXED)
ERYTHROS(MIXED) --> 2 ACETI-01(MIXED)
GLYCOLAL(MIXED) --> ACETI-01(MIXED)
DEXTR-01(MIXED) --> 5-HYD-01(MIXED) + 3 WATER(MIXED)
D-FRU-01(MIXED) --> 5-HYD-01(MIXED) + 3 WATER(MIXED)
5-HYD-01(MIXED) + 2 WATER(MIXED) --> LEVUL-01(MIXED) + FORMI-01(MIXED)
5-HYD-01(MIXED) --> FURFU-01(MIXED) + FORMA-01(MIXED)
96
xy.wshs
xy.fu
Kinetic
Kinetic
aa.ga
fu.wshs
wgsr
Kinetic
Kinetic
Kinetic
gu.t
Kinetic
gu.c
gu.ch
gu.oc
Kinetic
Kinetic
Kinetic
c.oc
t.ch
b.ph
b.na
b.ch
b.t
ph.t
ph.c
ph.ch
c.t
na.ch
la.acry
acry.la
la.acet
acet.aa
acry.hpa
hpa.acry
acry.pa
hpa.glyco
fa.ga1
fa.ga2
aa.ga
pa.ga
glyco.ga
mf.aa
fu.ch
l.la
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
Kinetic
D-XYL-01(MIXED) --> ACETI-01(MIXED) + ACRYL-01(MIXED) + WATER(MIXED)
D-XYL-01(MIXED) --> FURFU-01(MIXED) + 3 WATER(MIXED)
ACETI-01(MIXED) + ACRYL-01(MIXED) + WATER(MIXED) --> 5 CARBO-02(MIXED) + 5
HYDRO-01(MIXED)
FURFU-01(MIXED) + 2 WATER(MIXED) --> ACRYL-01(MIXED) + ACETI-01(MIXED)
CARBO-02(MIXED) + WATER(MIXED) --> CARBO-01(MIXED) + HYDRO-01(MIXED)
1.714 GUAIA-01(MIXED) --> BIPHE-01(MIXED) + 1.857 WATER(MIXED) + 0.786
OXYGE-01(MIXED)*
0.8571 GUAIA-01(MIXED) + 0.3571 OXYGE-01(MIXED) --> CATECHOL(MIXED) +
0.4286 WATER(MIXED)
GUAIA-01(MIXED) --> CHAR(NC)
GUAIA-01(MIXED) --> O-CRE-01(MIXED) + 0.5 OXYGE-01(MIXED)
1.16667 CATECHOL(MIXED) + 0.5 WATER(MIXED) --> O-CRE-01(MIXED) + 0.916667
OXYGE-01(MIXED)
BIPHE-01(MIXED) --> CHAR(NC)
BENZE-01(MIXED) + 0.5 OXYGE-01(MIXED) --> PHENO-01(MIXED)
2 BENZE-01(MIXED) --> NAPHT-01(MIXED) + ETHYL-01(MIXED)
BENZE-01(MIXED) --> CHAR(NC)
2 BENZE-01(MIXED) + 0.5 OXYGE-01(MIXED) --> BIPHE-01(MIXED) + WATER(MIXED)
2 PHENO-01(MIXED) --> BIPHE-01(MIXED) + WATER(MIXED) + 0.5 OXYGE-01(MIXED)
PHENO-01(MIXED) + 0.5 OXYGE-01(MIXED) --> CATECHOL(MIXED)
PHENO-01(MIXED) --> CHAR(NC)
2 CATECHOL(MIXED) --> BIPHE-01(MIXED) + WATER(MIXED) + 1.5 OXYGE-01(MIXED)
NAPHT-01(MIXED) --> CHAR(NC)
LACTI-01(MIXED) --> ACRYL-01(MIXED) + WATER(MIXED)
ACRYL-01(MIXED) + WATER(MIXED) --> LACTI-01(MIXED)
LACTI-01(MIXED) --> ACETA-01(MIXED) + CARBO-02(MIXED) + WATER(MIXED)
ACETA-01(MIXED) + WATER(MIXED) --> ACETI-01(MIXED) + HYDRO-01(MIXED)
ACRYL-01(MIXED) + WATER(MIXED) --> 3HPA(MIXED)
3HPA(MIXED) --> ACRYL-01(MIXED) + WATER(MIXED)
ACRYL-01(MIXED) + WATER(MIXED) --> PROPI-01(MIXED) + 0.5 OXYGE-01(MIXED)
3HPA(MIXED) + 0.75 OXYGE-01(MIXED) --> 1.5 GLYCO-01(MIXED)
FORMI-01(MIXED) --> CARBO-01(MIXED) + HYDRO-01(MIXED)
FORMI-01(MIXED) --> CARBO-02(MIXED) + WATER(MIXED)
ACETI-01(MIXED) --> METHANE(MIXED) + CARBO-01(MIXED)
PROPI-01(MIXED) + WATER(MIXED) --> 3 CARBO-02(MIXED) + 4 HYDRO-01(MIXED)
GLYCO-01(MIXED) + WATER(MIXED) --> 2 CARBO-01(MIXED) + 3 HYDRO-01(MIXED)
METHYLFO(MIXED) --> ACETI-01(MIXED)
FURFU-01(MIXED) --> CHAR(NC)
LEVUL-01(MIXED) + WATER(MIXED) --> LACTI-01(MIXED) + ACETA-01(MIXED)
*For reactions in the presence of Oxygen: It should be noted that Oxygen
was used as mass balance agent without being in rate equations and
purged after the reactor, hence does not have influence on kinetics.
97
C- RGibss reaction stoichiometries (for Lignin Pathway)
1- 650 ℃, 250 bars
Rxn Reaction
No. type
Stoichiometry
GUAIA-01(MIXED) + 5.36262 WATER(MIXED) --> 2.81059 HYDRO-01(MIXED) + 3.63874
CARBO-01(MIXED) + 0.0851501 CARBO-02(MIXED) + 3.27573 METHANE(MIXED) +
1
Kinetic 0.000195485 ETHAN-01(MIXED)
BIPHE-01(MIXED) + 11.2359 WATER(MIXED) --> 3.61427 HYDRO-01(MIXED) + 5.54694
CARBO-01(MIXED) + 0.141988 CARBO-02(MIXED) + 6.30995 METHANE(MIXED) +
2
Kinetic 0.000563771 ETHAN-01(MIXED)
PHENO-01(MIXED) + 5.04449 WATER(MIXED) --> 2.16141 HYDRO-01(MIXED) + 2.98613
CARBO-01(MIXED) + 0.0722316 CARBO-02(MIXED) + 2.94123 METHANE(MIXED) +
3
Kinetic 0.000204897 ETHAN-01(MIXED)
BENZE-01(MIXED) + 5.40379 WATER(MIXED) --> 1.87729 HYDRO-01(MIXED) + 2.66718
CARBO-01(MIXED) + 0.0694326 CARBO-02(MIXED) + 3.26281 METHANE(MIXED) +
4
Kinetic 0.000290222 ETHAN-01(MIXED)
2- 600℃, 250 bars
Rxn Reaction
No. type
Stoichiometry
GUAIA-01(MIXED) + 5.9489 WATER(MIXED) --> 4.06268 HYDRO-01(MIXED) + 3.8921
CARBO-01(MIXED) + 0.164702 CARBO-02(MIXED) + 2.94283 METHANE(MIXED) +
1
Kinetic 0.000187762 ETHAN-01(MIXED)
BIPHE-01(MIXED) + 11.9951 WATER(MIXED) --> 5.26526 HYDRO-01(MIXED) + 5.86036
CARBO-01(MIXED) + 0.274421 CARBO-02(MIXED) + 5.86408 METHANE(MIXED) +
2
Kinetic 0.000574953 ETHAN-01(MIXED)
PHENO-01(MIXED) + 5.49768 WATER(MIXED) --> 3.13528 HYDRO-01(MIXED) + 3.17897
CARBO-01(MIXED) + 0.13973 CARBO-02(MIXED) + 2.68089 METHANE(MIXED) + 0.000201878
3
Kinetic ETHAN-01(MIXED)
BENZE-01(MIXED) + 5.79849 WATER(MIXED) --> 2.732 HYDRO-01(MIXED) + 2.83188
CARBO-01(MIXED) + 0.134729 CARBO-02(MIXED) + 3.0328 METHANE(MIXED) + 0.000296391
4
Kinetic ETHAN-01(MIXED)
D- Component List
Component
ID
Type
Component name
WATER
Conventional WATER
CELLOBIO Conventional
Alias
H2O
*
98
D-XYL-01
GUAIA-01
Conventional D-XYLOSE
Conventional GUAIACOL
GLYCINE
GLYCOGLY
GLYCOERY
DEXTR-01
Conventional GLYCINE
Conventional
Conventional
Conventional DEXTROSE
D-FRU-01
GLYCERAL
GLYCOLAL
ERYTHROS
Conventional D-FRUCTOSE
Conventional
Conventional
Conventional
1,3-DIHYDROXY-2Conventional PROPANONE
DIHYDROX
LEVOG-01
PYRUV-01
Conventional LEVOGLUCOSAN
Conventional PYRUVIC-ALDEHYDE
55-HYD-01
Conventional HYDROXYMETHYLFURFURAL
FURFU-01 Conventional FURFURAL
FORMA-01 Conventional FORMALDEHYDE
METHYLFO Conventional METHYL-FORMATE
MAPLELAC Conventional
PHENO-01 Conventional PHENOL
O-CRE-01
Conventional O-CRESOL
BENZE-01 Conventional BENZENE
CATECHOL Conventional 1,2-BENZENEDIOL
NAPHT-01 Conventional NAPHTHALENE
PHENYLPH Conventional
BIPHE-01
Conventional DIPHENYL
FORMI-01 Conventional FORMIC-ACID
ACETI-01
Conventional ACETIC-ACID
GLYCO-01 Conventional GLYCOLIC-ACID
LACTI-01
Conventional LACTIC-ACID
PROPI-01
Conventional PROPIONIC-ACID
ACRYL-01
Conventional ACRYLIC-ACID
ACETA-01 Conventional ACETALDEHYDE
3HPA
Conventional
LEVUL-01
Conventional LEVULINIC-ACID
METHANOL Conventional METHANOL
AMMONIA Conventional AMMONIA
HYDRO-01 Conventional HYDROGEN
CARBO-01 Conventional CARBON-DIOXIDE
METHANE Conventional METHANE
CARBO-02 Conventional CARBON-MONOXIDE
ETHAN-01 Conventional ETHANE
ASH
Conventional POTASSIUM-CHLORIDE
C5H10O5
C7H8O2-E1
C2H5NO2D1
C6H12O6
C6H12O6N1
C3H6O3-N2
C6H10O5N1
C3H4O2
C6H6O3-N5
C5H4O2
CH2O
C2H4O2-2
C6H6O
C7H8O-3
C6H6
C6H6O2-E1
C10H8
C12H10
CH2O2
C2H4O2-1
C2H4O3-D1
C3H6O3-D1
C3H6O2-1
C3H4O2-1
C2H4O-1
C5H8O3-D1
CH4O
H3N
H2
CO2
CH4
CO
C2H6
KCL
99
CHAR
ETHYL-01
OXYGE-01
NITROGEN
ARGON
Nonconventional
Conventional ETHYLENE
Conventional OXYGEN
Conventional NITROGEN
Conventional ARGON
C2H4
O2
N2
AR
*Empty Alias section means user defined compounds
100
101