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

Area of Science:

• Polymer Chemistry
• Computational Chemistry
• Chemical Engineering

Background:

• Lignin and polymer reaction kinetics are complex due to heterogeneous bond structures.
• Conventional kinetic Monte Carlo (kMC) simulations are computationally expensive, evaluating low-probability events.
• This inefficiency hinders accurate modeling of lignin valorization processes.

Purpose of the Study:

• To develop an accelerated simulation framework for multiscale lignin fractionation.
• To reduce computational costs in kinetic modeling of complex polymer systems.
• To enable efficient and data-independent modeling for lignin valorization.

Main Methods:

• Developed a threshold-filtered kMC framework.
• Implemented an Arrhenius-type activation energy threshold to exclude kinetically irrelevant events.
• Applied the framework to simulate lignin molar mass and S/G ratio evolution.

Main Results:

• The threshold-filtered kMC framework significantly reduces CPU time by orders of magnitude.
• The model maintains high fidelity in predicting lignin molar masses and S/G ratios.
• Kinetically irrelevant events were successfully excluded, optimizing simulation efficiency.

Conclusions:

• The threshold-filtered kMC framework provides a scalable and efficient tool for lignin valorization.
• This approach enables accurate, data-independent modeling of complex reaction networks.
• The strategy facilitates process-level optimization and control in polymer reaction systems.