(492c) A Computational Approach to Enhance the Economic Viability of Acid Fractionation Via Multiscale Modeling and Economic Model Predictive Control
AIChE Annual Meeting
Wednesday, November 10, 2021 - 1:08pm to 1:27pm
The economic feasibility of bioconversion is dependent on the cost of operating conditions (e.g., enzyme dosage and reaction temperature) and the revenue from the fractionated lignocellulose. However, finding an economically viable pathway of fractionation is impeded by the lack of understanding on the reaction process. For example, even though the recalcitrance of lignocellulose is determined by many factors such as cellulose accessibility, lignin content, and degree of polymerization (DP) of cellulose, the contribution of each one has not been fully elucidated, thereby veiling the profitability [12-13]. Moreover, even though the operating conditions play a crucial role in determining the quality of the fractionated lignocellulosic components (e.g., purity, DP, and yield), limited effort has been made to decipher the effect of the operating conditions on the economy of the lignocellulose fractionation [14-15]. It is attributed to the insufficient understanding of the nanoscale events during the fractionation, which is attributed to the fact that the previously developed deterministic models are not able to describe the potential pathways of fractionation at a molecular level, such as the evolution of DP and accessible surface areas.
Motivated by the limitation, in this work, a multiscale model was developed to capture the multidimensional phenomena during the acid fractionation under lignin-derivable phenol-4-sulfonic acid (PSA) solvent. First, a first-principle model was developed to describes the macroscopic properties such as concentrations of lignin, cellulose, and PSA during the fractionation process. Then, a kinetic Monte Carlo (kMC) algorithm, which is capable of capturing microscopic phenomena such as the evolution of DP and component accessibility, was integrated with the first-principle model. Specifically, since the lignin and cellulose are cleaved into smaller compounds during the fractionation process, these microscopic events were described by taking into account random and specific chain scission events in the kMC algorithm. Thereafter, reaction parameters were estimated, and the developed multiscale model was validated by experimental data sets. The developed model was then incorporated into the economic model predictive control (EMPC) system to compute the optimal input sequences which bring the maximum profit while balancing the operating cost (i.e., heat and PSA usage) and the expected revenue (i.e., glucose and lignin recovery).
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