Optimal control of dilute acid pretreatment and enzymatic hydrolysis for processing lignocellulosic feedstock

Lignocellulosic feedstock is one of the potential renewable sources for producing ethanol for transportation. The process steps viz., acid pretreatment and enzymatic hydrolysis in bio-chemical process route are intended to produce fermentable sugars, which can be readily fermented for producing ethanol. However, the dilute acid pre-treatment and enzymatic hydrolysis process steps are found to be economically inefficient. The present work aims at optimizing these process steps for improving the process performance. Such optimization is expected to increase conversion, reduce energy or material requirement, thereby improving the economics. The kinetic models of acid pretreatment and enzymatic hydrolysis for lignocellulosic feedstock processing are adapted from literature. Subsequently, these kinetic models are augmented by associated mass and energy balances, to develop a batch reactor model and fed-batch reactor model for dilute acid pretreatment and enzymatic hydrolysis processes, respectively. Optimal control with Pontryagin's maximum principle has been implemented to determine the optimal time dependent profiles of heating and cooling fluid flow rates and operating temperatures for acid pretreatment and substrate feed rate profile for enzymatic hydrolysis to optimize the respective processes performance. Different objective functions such as maximizing concentration of desired product, minimizing the batch time, and maximizing profit have been considered. The simulation results yielded an increase of 6.7% and 8.8% in final concentration of desired product; 43% and 42.5% reduction in batch processing time for pretreatment and enzymatic hydrolysis processes, respectively. Finally, the simulation results have also provided optimal operating policies which have increased the profit of pretreatment by 124% and enzymatic hydrolysis by 150%, thereby improving the techno-economic feasibility for processing lignocellulosic feedstock. (C) 2017 Elsevier Ltd. All rights reserved