Dynamically modelling enzymatic hydrolysis of
lignocellulosic substrates
Chuanji Fang, Jens Ejbye Schmidt, Jorge Rodriguez, Mette Thomsen
[email protected], [email protected], [email protected], [email protected]
iEnergy, Department of Chemical and Environmental Engineering, Masdar Institute of Science and Technology, PO box 54224, Masdar City, Abu Dhabi, UAE
Background
Abstract
Enzymatic hydrolysis is one the crucial steps of conversion of lignocellulosic biomass to
Mechanism of cellulolytic enzymes2
produce biochemical and biofuels. Accurate quantification of substrate-enzyme interactions is
Enzymatic catalysis reactions
highly required for the sake of enzyme (e.g. enzyme cocktail compositions) and process (e.g.
Glucan
reaction conditions and reactor deign) optimization. A modified dynamic model of enzymatic
r1
hydrolysis based on Prunescu’s model1 was proposed by incorporation of the enzyme
r2
component (Auxiliary activity family 9 (AA9), formerly glycoside hydrolase (GH61)) which was
r3
not included in the original model. The model was fitted against literature experimental data2
r4
with good predications of the glucose conversion for 72 hours enzymatic hydrolysis. Sensitivity
analysis shows that rate constant of cellulose-to-glucose reaction catalyzed by
cellobiohydrolases (CBHs) & endo-β-glucanase (EGs), glucose inhibition to CBHs & EGs,
Figure 2 Enzymatic catalysis reactions by
CBH&EG, BG, xylanase and GH61
maximum quantities of adsorbed CBHs & EGs and the adsorption constant of CBHs & EGs are
four main parameters affecting predictions of the model.
Methods
Model simulation
Figure 1 Schemical Catalysis mechanism of cellulytic enzymes (CBHs, EGs
,β-1,4-glucosidase(BG), GH61 and endo-xylanase)
Inputs & Parameters
Inputs
Symbols Unit
Cellulose concentration
Xylan concentration
Lignin concentration
Glucose concentration
Cellobiose concentration
Xylose concentration
Enzyme concentration in hydrolysate
CCS
CXS
CLS
CG
CC
CX
CE(h)
g kg
-1
g kg
g kg -1
g kg -1
g kg -1
g kg -1
g kg -1 (hydrolysate)
Furfural
CF
g kg
Parameters
Symbols Unit
1. Independent fixed parameters
EG&CBH
BG
Xylanase
Other enzyme components
2. Estimated parameters
Reaction rate constant of r1
Reaction rate constant of r2
Reaction rate constant of r3
Reaction rate constant of r4
Maximum adsorbed enzyme of E1
Maximum adsorbed enzyme of E2
Maximum adsorbed enzyme of E3
Inhibition of glucose on r1
Inhibition of glucose on r2
Inhibition of glucose on r3
Inhibition of glucose on r4
Inhibition of cellobiose on r1
Inhibition of cellobiose on r2
Inhibition of cellobiose on r4
Overall inhibition on r3
Inhibition of xylose on r1
Inhibition of xylose on r2
Inhibition of xylose on r3
Inhibition of xylose on r4
Inhibition of fufural on r1
Inhibition of fufural on r2
Inhibition of fufural on r3
Inhibition of fufural on r4
Adsorption constant of E1
Adsorption constant of E2
Adsorption constant of E3
Reaction kinetics1
Model simulation
-1
Mass balance1
α1
α2
α3
α4
-
K1
K2
K3
K4
E M1
E M2
E M3
I G1
I G2
I G3
I G4
I c1
I c2
I c4
I3
I x1
I x2
I x3
I x4
I F1
I F2
I F3
I F4
K A1
K A2
K A3
kg g -1 s -1
kg g -1 s -1
kg g -1 s -1
kg g -1 s -1
-1
g kg
g kg -1
g kg -1
g kg -1
g kg -1
g kg -1
g kg -1
-2
g kg
-1
g kg
-1
g kg
g kg -1
g kg -1
g kg -1
g kg -1
g kg -1
g kg -1
-
Parameters Estimation
Results
-1
 The dynamic kinetic model of enzymatic hydrolysis of
Wheat straw4
Hydrothermal pretreatment
lignocellulosic substrate is simulated.
 However, the model has bad fitting against experimental
data.
Parameters Estimation
 The predication improves after parameters estimation.
Reaction time
Time, s
State variables
Kinetics
Reaction Reactio Reactio
rate of
n rate of n rate of
Cellulose Cellulos cellobio
Cellulose Xylan Lignin Glucose Cellobiose Xylose to
e to
se to
, g/L
, g/L , g/L , g/L
, g/L
, g/L cellobiose glucose glucose
Reactio
n rate of
xylan to Cellulose Cellobiose,
xylose , g/L
g/L
CCS0
r4
CXS0
CLS0
CG
CC
CX
r1
r2
r3
Mass balance
dCCS/dt
dCC/dt
Glucose, Xylan,
g/L
g/L
Xylose,
g/L
dCG/dt dCXS/dt dCX/dt
Model modification
Outputs
Glucose concentration
 The modified model shows significant better fitting
compared with simulated model.
Model modification
1.25X3
𝒓′𝟏 = 𝟏. 𝟐𝟓𝒓𝟏
Sensitivity analysis
Challenges
𝐶𝑉: 𝐶𝑒𝑙𝑙𝑢𝑙𝑜𝑠𝑒 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛;
𝑇𝑆: Total Solids
Arundo donax5
Sensitivity analysis
Steam explosion
 Parameters related to reactions catalyzed by
 Poor prediction is found on different
CBH & EG are the most influential factors.
biomass and pretreatment method.
Conclusions
 The modified model significantly improves the fitting compared with simulated one.
 Sensitivity analysis indicates that rate constant of cellulose-to-glucose reaction catalyzed by
CBHs & EGs, glucose inhibition to CBHs & EGs, maximum quantities of adsorbed CBHs & EGs
and the adsorption constant of CBHs & EGs are four main parameters affecting predictions of
the model.
 Challenges of the modified model is the specificity to the substrates and pretreatment methods.
Acknowledgement
[1]
[2]
[3]
[4]
[5]
The Masdar Institute of Science and Technology (MI) and Massachusetts Institute of
Technology (MIT) flagship project: Biorefinery-Integrated sustainable process for biomass
www.postersession.com
conversion to biomaterials, biofuels and fertilizer.
R. M. Prunescu and G. Sin, Bioresour. Technol., vol. 150, pp. 393-403, Dec. 2013.
P. V Harris, D. Welner, K. C. McFarland, et al., Biochemistry, vol. 49, no. 15, pp. 3305–16, Apr. 2010.
S. J. Horn, G. Vaaje-Kolstad, B. Westereng, and V. G. Eijsink. Biotechnol. Biofuels, vol. 5, no. 1, p. 45, Jan. 2012.
D. Cannella, C.-W. C. Hsieh, C. Felby, and H. Jørgensen, Biotechnol. Biofuels, vol. 5, no. 1, p. 26, Jan. 2012.
I. De Bari, F. Liuzzi, A. Villone, and G. Braccio, Appl. Energy, vol. 102, pp. 179-189, Feb. 2013.
www.postersessi on.com
References

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