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 <em>K</em><sub>2IG2</sub>, <em>K</em><sub>1IG</sub>, <em>K</em><sub>2IG</sub>, <em>K</em><sub>1IA</sub>, and <em>K</em><sub>3IA</sub> 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.
Highlights
Global concerns over climate impacts of greenhouse gas (GHG) emissions have led to the pursuit of low carbon intensity alternatives to fossil fuels [1]
The optimization model goal was to minimize the residual errors between the kinetic model predictions and experimental data
We developed a multi-objective formulation of the kinetic model that simultaneously minimizes residual errors between predicted and observed yields of glucose and cellobiose
Summary
Global concerns over climate impacts of greenhouse gas (GHG) emissions have led to the pursuit of low carbon intensity alternatives to fossil fuels [1] These efforts led to the commercialization of corn grain and sugarcane based ethanol to replace petroleum derived fuels in the transportation sector [2]. Economic and performance challenges have limited the commercial adoption of lignocellulosic ethanol technologies [6] Lignocellulosic biomass such as corn stover is mainly composed of cellulose, hemicellulose and lignin intertwined by a complex matrix formed by these three biopolymers. Enzymatic hydrolysis combined with feedstock pretreatment is preferred over other chemical hydrolysis for its higher yield, minimal byproduct formation, low energy requirements, and mild operating conditions [8,9] This technique faces technical challenges and the process is not yet economically feasible [10,11]
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