(562e) A Multi-Scale Kinetic Modeling and Optimal Control Strategy for an Effective Lignin Fractionation Process
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Forest and Plant Bioproducts Division
Computational method aided biomass and waste utilization
Monday, November 6, 2023 - 9:12am to 9:30am
The utilization of lignin has been emphasized to improve the overall profitability of the biorefinery industry [3, 4]. Although numerous experimental developments have been made [5], a knowledge gap remains in the comprehensive understanding of lignin dynamics in reaction systems. Existing mathematical models [6], which focus solely on de/repolymerization processes with dissolved lignin chains, cannot fully explain the overall biorefinery dynamics. Moreover, real-time measurements of key parameters such as lignin content and molecular weight distribution are still in their infancy [7] and cannot be performed in full-scale pulp digesters. Therefore, a high-fidelity mathematical model and process control strategy are necessary for feasible biomass processing.
Motivated by this need, we introduce a multiscale model that elaborates on both macroscopic (delignification) and microscopic (de/repolymerization) reactions, based on fractionation experiment using a novel solvent, phenol-4-sulfonic acid (PSA) [8]. We employed a bilayer framework, consisting of ordinary differential equations for delignification and a kinetic Monte Carlo (kMC) algorithm for de/repolymerization. Following the successful development of a data-driven kinetic model, we applied a model predictive control to optimize the reaction, using a soft sensor (i.e., Kalman filter) to estimate the primary variables (lignin content and average molecular weight) from the available data (solution temperature).
We verified our model against experimental data and validated our control framework using a real pulp digester, showcasing the seamless integration of theoretical studies with experiments in our work. Our process control demonstrated low offsets from set points for key variables, including lignin content and the average molecular weight of lignin chains. This study highlights the successful combination of experimental work and simulation, leading to a deeper understanding of lignin dynamics in reaction systems and ultimately enabling a lignin fractionation approach in biorefineries.
Literature cited:
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