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

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

Special Issues: Advances in Production of Biofuels

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 K2IG2, K1IG, K2IG, K1IA, and K3IA 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.
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Keywords 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. AIMS Energy, 2016, 4(1): 52-67. doi: 10.3934/energy.2016.1.52


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Copyright Info: 2016, Mark Mba Wright, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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