A study of methanol-to-olefins packed bed reactor performance using particle-resolved CFD and machine learning

In this study, particle-resolved computational fluid dynamics (CFD) simulations were performed to analyze fluid flow, mass transport, and reaction phenomena in methanol-to-olefins packed bed reactors with diverse cylindrical configurations and operating conditions. Utilizing validated CFD data, data-driven surrogate models were developed based on several representative machine learning (ML) techniques. Comprehensive training and optimization of ML model hyperparameters were performed, followed by a comparative assessment of their capabilities to predict reactor performance. Subsequently, data-driven surrogate models together with CFD simulations were applied to optimize catalyst structure design and operating conditions. Finally, a hybrid approach was developed that couples the ML-aided data-driven model with a genetic algorithm-based multi-objective optimization. The resulting hybrid method was applied to find the Pareto-optimal compromise between pressure drop and light olefins yield.