(596d) Enhancing Glucose Yield By Modeling of Cellulose Accessibility: Application of Model Predictive Control to Alkaline Pretreatment for Bioethanol Production
We developed a multiscale model for biomass pretreatment that can describe the dynamic evolution of cellulose accessible area of biomass. Specifically, the degradation events of several biomass components under alkali chemical solvents (called pretreatment) are modeled by developing a new kMC algorithm, and the transport phenomena of the continuous phases are captured by a mathematical model that has been widely used to model pulp digesters; this is reasonable as pulp digester and pretreatment processes share many common features and fundamental chemical reactions. Then, we modeled enzymatic hydrolysis, which follows pretreatment and converts pretreated lignocellulosic biomass to fermentable sugars, by adopting a kinetic model from the literature to calculate the glucose yield under given enzymatic hydrolysis conditions (e.g., hydrolysis time, concentration of enzymes) [9-10]. Therefore, by integrating the proposed multiscale model of pretreatment and the existing kinetic model of enzymatic hydrolysis, we were able to predict the glucose production over different pretreatment temperatures. Once this integrated model was validated against experimental data from the literature , a reduced-order model was developed to design a model-based feedback controller for enhanced cellulose accessible area that ultimately leads to higher glucose yield. Within the controller, the solvent temperature that directly affects the biomass is manipulated while minimizing the energy used for heating during pretreatment. The implementation of the control framework demonstrated that the proposed modeling and control approaches improved the glucose yield by 46% while consuming only 7.2% more heat energy than a conventional constant-temperature pretreatment method.
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