While lignin has garnered significant research interest for its abundance and versatility, its complicated structure poses a challenge to understanding its underlying reaction kinetics and optimizing various lignin characteristics. In this regard, mathematical models, especially the multiscale kinetic Monte Carlo (kMC) method, have been devised to offer a precise analysis of fractionation kinetics and lignin properties. The kMC model effectively handles the simulation of all particles within the system by calculating reaction rates between species and generating a rate-based probability distribution. Then, it selects a reaction to execute based on this distribution. However, due to the vast number of lignin polymers involved in the reactions, the rate calculation step becomes a computational bottleneck, limiting the model's applicability in real-time control scenarios. To address this, the machine learning (ML) technique is integrated into the existing kMC framework. By training an artificial neural network (ANN) on the kMC data sets, we predict the probability distributions instead of repeatedly calculating them over time. Subsequently, the resulting ANN-accelerated multiscale kMC (AA-M-kMC) model is incorporated into a model predictive controller (MPC), facilitating real-time control of intricate lignin properties. This innovative approach effectively reduces the computational burden of kMC and advances lignin processing methods.
© 2024 The Authors. Published by American Chemical Society.