Threshold-Filtered Kinetic Monte Carlo Simulation for Real-Time Simulation and Control of Biomass Fractionation

For lignin and other polymer reaction systems, reaction kinetics are inherently complex. Individual bond-cleavage reactions exhibit a wide distribution of activation energies due to structural heterogeneity among β-O-4 linkages. In conventional kinetic Monte Carlo (kMC) simulations, reactions with high activation energies, whose probabilities are extremely low, are still evaluated at every step, leading to substantial computational cost with negligible impact on system evolution. To overcome this limitation, we developed a threshold-filtered kMC framework that accelerates multiscale lignin fractionation simulations by excluding kinetically irrelevant events using an Arrhenius-type activation energy threshold. The model preserves its fidelity while reducing CPU time by orders of magnitude compared with conventional approaches. It accurately predicts the evolution of lignin molar masses and S/G ratios under varying reaction conditions. This strategy enables efficient, data-independent modeling of complex reaction networks and establishes a scalable tool for process-level optimization and control in lignin valorization systems.