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.

中文翻译：

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使用粒子分辨 CFD 和机器学习研究甲醇制烯烃填充床反应器性能


在这项研究中，进行了粒子解析计算流体动力学（CFD）模拟，以分析具有不同圆柱形配置和操作条件的甲醇制烯烃填充床反应器中的流体流动、质量传递和反应现象。利用经过验证的 CFD 数据，基于几种代表性机器学习 (ML) 技术开发了数据驱动的替代模型。对机器学习模型超参数进行了全面的训练和优化，然后对其预测反应器性能的能力进行了比较评估。随后，应用数据驱动的替代模型和 CFD 模拟来优化催化剂结构设计和操作条件。最后，开发了一种混合方法，将机器学习辅助的数据驱动模型与基于遗传算法的多目标优化结合起来。应用所得的混合方法来寻找压降和轻质烯烃产率之间的帕累托最优折衷。