Accelerated Molecular Simulation-Based Model Predictive Control for Regulating Molecular Weights and Monomeric Ratios in Biomass Fractionation

The complex structure of lignin presents significant challenges in understanding its reaction kinetics and optimizing its properties. Thus, multiscale kinetic Monte Carlo (kMC) models have been developed to provide detailed insights into the fractionation processes. The kMC calculates reaction rates between species, generates a rate-based probability distribution, and executes the most probable reactions at each time step. Despite its ability to handle nonlinear dynamics, the high computational demand associated with rate calculations makes real-time control challenging. Therefore, an artificial neural network (ANN) has been trained on the kMC input/output data to replace the repetitive rate calculation step of the kMC algorithm. This accelerated kMC model predicts reaction rates given the current status instead of calculating them, thereby significantly reducing the computational loads of the original kMC model. Integrated into a model predictive control (MPC) framework, the accelerated kMC enables real-time control of lignin properties, enhancing the efficiency and feasibility of the lignin fractionation process.