Stochastic optimization of enzymatic hydrolysis of lignocellulosic biomass. (6th April 2020)

• Record Type: Journal Article
• Authors: Fenila, F.; Shastri, Yogendra
• Is Part Of: Computers & chemical engineering. Volume 135(2020)
• Publication Date: 2020-04-06
• Subjects: Hydrolysis -- Uncertainties -- Optimization -- Optimal control,; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.106776
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Maximization of glucose and minimization of variance problems are solved. Uncertainty in feedstock composition and kinetic parameters considered. Decrease in optimal temperature with batch time. Upto 7.7% improvement in glucose concentration using stochastic optimization. Upto 60% improvement in glucose concentration using stochastic optimal control. Abstract: Uncertainties in feedstock composition and kinetic parameters impact the performance of enzymatic hydrolysis of lignocellulosic biomass. Operating strategies need to be accordingly modified to achieve higher yield of glucose. Therefore, stochastic optimization and optimal control problems were formulated to maximize glucose concentration and minimize variance. The optimal temperature for maximization of glucose concentration reduced in the presence of uncertainties. For uncertainties in kinetic parameters, the optimal temperature reduced from 323.34 K to 317.07 K for a batch time of 12 h, and the glucose concentration increased by 7.7% when the stochastic optimal temperature was used instead of deterministic temperature. Similarly, for the objective of minimization of variance in glucose concentration at the end of the batch, stochastic optimization reduced the variance by 89% as compared to deterministic optimization. Stochastic optimal control resulted in up to 60% improvement in mean glucose concentration in comparison to the deterministic optimal control.