Advancing biomass fractionation with real-time prediction of lignin content and MWd: A kMC-based multiscale model for optimized lignin extraction

Abstract

Recently, lignin has garnered significant research attention due to its abundance in nature. However, lignin is viewed as a recalcitrance factor as it impedes the overall biomass fractionation. In this regard, harsh operating conditions have been applied for the effective separation of the biomass components but they may cause substantial lignin degradation. Another problem is that the overall kinetics of lignin reactions remain limited since current models primarily focus on the cellulose fiber. These pose a challenge when developing effective fractionation strategies for industrial lignin extraction. To this end, we propose a multiscale model and develop a controller to determine the optimal operation strategy. In terms of lignin, delignification and de/repolymerization happen simultaneously but in different length and time scales. We adopted a bilayer structure of the ODEs and kinetic Monte Carlo (kMC) algorithm, accounting for the multiscale reaction kinetics. Our model provides the key outputs including the lignin content in the bulk chip and lignin molecular weight distribution, which were validated with the experiments. Subsequently, we developed a reduced-order model (ROM) for soft sensor design and formulated a model predictive controller (MPC) to determine the optimal operation strategy and then maximize the profitability.