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

Abstract

AbstractIn 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.