A novel optimization approach to estimating kinetic parameters of the enzymatic hydrolysis of corn stover

Fenglei Qi; Mark Mba Wright; Fenglei Qi; Mark Mba Wright

doi:10.3934/energy.2016.1.52

AIMS Energy

2016, Volume 4, Issue 1: 52-67. doi: 10.3934/energy.2016.1.52

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A novel optimization approach to estimating kinetic parameters of the enzymatic hydrolysis of corn stover

Fenglei Qi ,
Mark Mba Wright ^,

Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA

Received: 02 November 2015 Accepted: 07 January 2016 Published: 13 January 2016

Enzymatic hydrolysis is an integral step in the conversion of lignocellulosic biomass to ethanol. The conversion of cellulose to fermentable sugars in the presence of inhibitors is a complex kinetic problem. In this study, we describe a novel approach to estimating the kinetic parameters underlying this process. This study employs experimental data measuring substrate and enzyme loadings, sugar and acid inhibitions for the production of glucose. Multiple objectives to minimize the difference between model predictions and experimental observations are developed and optimized by adopting multi-objective particle swarm optimization method. Model reliability is assessed by exploring likelihood profile in each parameter space. Compared to previous studies, this approach improved the prediction of sugar yields by reducing the mean squared errors by 34% for glucose and 2.7% for cellobiose, suggesting improved agreement between model predictions and the experimental data. Furthermore, kinetic parameters such as K_2IG2, K_1IG, K_2IG, K_1IA, and K_3IA are identified as contributors to the model non-identifiability and wide parameter confidence intervals. Model reliability analysis indicates possible ways to reduce model non-identifiability and tighten parameter confidence intervals. These results could help improve the design of lignocellulosic biorefineries by providing higher fidelity predictions of fermentable sugars under inhibitory conditions.
- Multi-objective regression,
- enzymatic hydrolysis kinetics,
- parameter estimation,
- multi-objective particle swarm optimization,
- likelihood profile
Citation: Fenglei Qi, Mark Mba Wright. A novel optimization approach to estimating kinetic parameters of the enzymatic hydrolysis of corn stover[J]. AIMS Energy, 2016, 4(1): 52-67. doi: 10.3934/energy.2016.1.52

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Abstract

Enzymatic hydrolysis is an integral step in the conversion of lignocellulosic biomass to ethanol. The conversion of cellulose to fermentable sugars in the presence of inhibitors is a complex kinetic problem. In this study, we describe a novel approach to estimating the kinetic parameters underlying this process. This study employs experimental data measuring substrate and enzyme loadings, sugar and acid inhibitions for the production of glucose. Multiple objectives to minimize the difference between model predictions and experimental observations are developed and optimized by adopting multi-objective particle swarm optimization method. Model reliability is assessed by exploring likelihood profile in each parameter space. Compared to previous studies, this approach improved the prediction of sugar yields by reducing the mean squared errors by 34% for glucose and 2.7% for cellobiose, suggesting improved agreement between model predictions and the experimental data. Furthermore, kinetic parameters such as K_2IG2, K_1IG, K_2IG, K_1IA, and K_3IA are identified as contributors to the model non-identifiability and wide parameter confidence intervals. Model reliability analysis indicates possible ways to reduce model non-identifiability and tighten parameter confidence intervals. These results could help improve the design of lignocellulosic biorefineries by providing higher fidelity predictions of fermentable sugars under inhibitory conditions.

References

[1]	Pachauri RK, Allen M, Barros V, et al. (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
[2]	MacDonald T, Yowell G, McCormack M (2001) US ethanol industry: production capacity outlook. California Energy Commission. P600-01-017.
[3]	Kazi FK, Fortman J, Anex R, et al. (2010) Techno-economic analysis of biochemical scenarios for production of cellulosic ethanol: Citeseer.
[4]	Kazi FK, Fortman JA, Anex RP, et al. (2010) Techno-economic comparison of process technologies for biochemical ethanol production from corn stover. Fuel 89: S20-S28. doi: 10.1016/j.fuel.2010.01.001
[5]	Winchester N, Reilly JM (2015) The feasibility, costs, and environmental implications of large-scale biomass energy. Energy Economics 51: 188-203. doi: 10.1016/j.eneco.2015.06.016
[6]	Brown TR, Brown RC (2013) A review of cellulosic biofuel commercial‐scale projects in the United States. Biofuels, bioprod bioref 7: 235-245. doi: 10.1002/bbb.1387
[7]	Chiaramonti D, Prussi M, Ferrero S, et al. (2012) Review of pretreatment processes for lignocellulosic ethanol production, and development of an innovative method. Biomass Bioenerg 46: 25-35. doi: 10.1016/j.biombioe.2012.04.020
[8]	Sun Y, Cheng J (2002) Hydrolysis of lignocellulosic materials for ethanol production: a review. Bioresource technol 83: 1-11. doi: 10.1016/S0960-8524(01)00212-7
[9]	Wald S, Wilke CR, Blanch HW (1984) Kinetics of the enzymatic hydrolysis of cellulose. Biotechnol bioeng 26: 221-230. doi: 10.1002/bit.260260305
[10]	Hayes DJ (2009) An examination of biorefining processes, catalysts and challenges. Catal Today 145: 138-151. doi: 10.1016/j.cattod.2008.04.017
[11]	Sin G, Meyer AS, Gernaey KV (2010) Assessing reliability of cellulose hydrolysis models to support biofuel process design—identifiability and uncertainty analysis. Comput chem eng 34: 1385-1392. doi: 10.1016/j.compchemeng.2010.02.012
[12]	Humbird D, Aden A (2009) Biochemical production of ethanol from corn stover: 2008 state of technology model: Citeseer.
[13]	Morales-Rodriguez R, Meyer AS, Gernaey KV, et al. (2011) Dynamic model-based evaluation of process configurations for integrated operation of hydrolysis and co-fermentation for bioethanol production from lignocellulose. Bioresource Technol 102: 1174-1184. doi: 10.1016/j.biortech.2010.09.045
[14]	Morales-Rodriguez R, Meyer AS, Gernaey KV, et al. (2012) A framework for model-based optimization of bioprocesses under uncertainty: Lignocellulosic ethanol production case. Comput Chem Eng 42: 115-129. doi: 10.1016/j.compchemeng.2011.12.004
[15]	Bansal P, Hall M, Realff MJ, et al. (2009) Modeling cellulase kinetics on lignocellulosic substrates. Biotechnol adv 27: 833-848.
[16]	Kadam KL, Rydholm EC, McMillan JD (2004) Development and validation of a kinetic model for enzymatic saccharification of lignocellulosic biomass. Biotechnol prog 20: 698-705. doi: 10.1021/bp034316x
[17]	Zheng Y, Pan Z, Zhang R, et al. (2009) Kinetic modeling for enzymatic hydrolysis of pretreated creeping wild ryegrass. Biotechnol bioeng 102: 1558-1569. doi: 10.1002/bit.22197
[18]	Tsai CT, Morales-Rodriguez R, Sin G, et al. (2014) A dynamic model for cellulosic biomass hydrolysis: A comprehensive analysis and validation of hydrolysis and product inhibition mechanisms. Appl biochem biotech 172: 2815-2837. doi: 10.1007/s12010-013-0717-x
[19]	Scott F, Li M, Williams DL, et al. (2015) Corn stover semi-mechanistic enzymatic hydrolysis model with tight parameter confidence intervals for model-based process design and optimization. Bioresource technol 177: 255-265. doi: 10.1016/j.biortech.2014.11.062
[20]	Box GE, Draper NR (1965) The Bayesian estimation of common parameters from several responses. Biometrika 52: 355-365. doi: 10.1093/biomet/52.3-4.355
[21]	Stewart WE, Caracotsios M, Sørensen JP (1992) Parameter estimation from multiresponse data. AIChE J 38: 641-650. doi: 10.1002/aic.690380502
[22]	Ziegel ER, Gorman JW (1980) Kinetic modelling with multiresponse data. Technometrics 22: 139-151. doi: 10.1080/00401706.1980.10486129
[23]	Conceição EL, Portugal AA (2012) Comparison of Two Robust Alternatives to the Box–Draper Determinant Criterion in Multiresponse Kinetic Parameter Estimation. Ind Eng Chem Res 51: 1118-1130.
[24]	Raue A, Kreutz C, Maiwald T, et al. (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 25: 1923-1929. doi: 10.1093/bioinformatics/btp358
[25]	Kim DW, Kim TS, Jeong YK, et al. (1992) Adsorption kinetics and behaviors of cellulase components on microcrystalline cellulose. J Ferment Bioeng 73: 461-466. doi: 10.1016/0922-338X(92)90138-K
[26]	Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. Evolutionary Computation, IEEE Transactions on 8: 256-279. doi: 10.1109/TEVC.2004.826067
[27]	Hindmarsh AC, Brown PN, Grant KE, et al. (2005) SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. ACM T Math Software 31: 363-396. doi: 10.1145/1089014.1089020

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