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

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

Validation of Inhibition Effect in the Cellulose Hydrolysis: a Dynamic Modelling Approach

Enzymatic hydrolysis is one of the main steps in the processing of bioethanol from lignocellulosic raw materials. However, complete understanding of the underlying phenomena is still under development. Hence, this study has focused on validation of the inhibition effects in the cellulosic biomass hydrolysis employing a dynamic mathematical model. A systematic framework for parameter estimation is used for model validation, which helps overcome the problem of parameter correlation. Data sets obtained from carefully designed enzymatic cellulose and cellobiose hydrolysis experiments, were used for parameter estimation (calibration) and validation purposes. The model predictions using calibrated parameters have shown good agreement with the validation data sets, which provides credibility to the model structure and the parameter values

Process development for platform chemical production from agricultural and forestry residues

As part of a bio-based economy, biorefineries are envisaged to sustainably produce platform chemicals via biochemical conversion of agricultural and forestry residues. However, supply risks, the recalcitrance of lignocellulosic biomass, and inhibitor formation during pre\uadtreatment impair the economic feasibility of such biorefineries. In this thesis, process design and assessment were developed with the aim of addressing these hurdles and improving the cost-effectiveness of lignocellulose-derived platform chemicals.To expand the feedstock base and reduce operational costs, logging residues served as underutilised and inexpensive raw material. The major impediment in converting logging residues was their high recalcitrance and low cellulose content, which resulted in low attainable ethanol titres during simultaneous saccharification and co-fermentation (SSCF). Pretreatment optimisation reduced inhibitor formation and recalcitrance, and led to enzymatic hydrolysis yields at par with those obtained for stem wood, despite the less favourable chemical composition. Upgrading logging residues with carbohydrate-rich oat hulls increased ethanol titres to >50\ua0g/L using batch SSCF at 20% WIS loadings, demonstrating the potential to further decrease downstream processing costs. To alleviate the toxicity of inhibitors generated during pretreatment, preadaptation was applied to Saccharomyces cerevisiae. Exposure to the inhibitors in the pretreated liquid fraction improved ethanol production during subsequent fermentation. Transferring the concept of preadaptation to lactic acid production by Bacillus coagulans cut the process times by half and more than doubled the average specific lactic acid productivity, showcasing how preadaptation could decrease operational costs.To assess the performance and robustness of process designs against process input variations, a multi-scale variability analysis framework was developed. The framework included models for bioprocess, flowsheet, techno-economic, and life cycle assessment. In a case study, multi-feed processes, in which solids and cells are fed to the process using model-based predictions, were more robust against variable cellulolytic activities than batch SSCFs in a wheat straw-based ethanol biorefinery. The developed framework can be used to identify robust biorefinery process designs, which simultaneously meet technological, economic, and environmental goals