Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

作     者：Haolei Zhang Aiqi Zhu Ji Xu Wei Ge Haolei Zhang;Aiqi Zhu;Ji Xu;Wei Ge

作者机构：State Key Laboratory of Mesoscience and EngineeringInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China State Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China Innovation Academy for Green ManufactureChinese Academy of SciencesBeijing 100190China 

出 版 物：《Particuology》 (颗粒学报（英文版）)

年 卷 期：2024年第89卷第6期

页      面：131-143页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 0817[工学-化学工程与技术] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 

基　　金：supported by the National Natural Science Foundation of China(grant Nos.22293024,22293021,and 22078330) the Youth Innovation Promotion Association,Chinese Academy of Sciences(grant No.2019050) 

主　　题：Discrete particle method Artificial intelligence Machine learning Particle swarm optimization Industrial reactor optimization 

摘      要：Design,scaling-up,and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience,which is usually time-consuming,high cost,and high *** numerical simulation can reproduce high resolution details of hydrodynamics,thermal transfer,and reaction process in reactors,it is still challenging for industrial reactors due to huge computational *** this study,by combining the numerical simulation and artificial intelligence(AI)technology of machine learning(ML),a method is proposed to efficiently predict and optimize the performance of industrial reactors.A gas–solid fluidization reactor for the methanol to olefins process is taken as an example.1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model,and thus,the reactor performance data set is *** develop an efficient reactor performance prediction model influenced by multiple factors,the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique,which has better performance than the methods based on the artificial neural ***,the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization *** show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield,while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure *** two objectives are thus conflicting,and the practical operations need to be compromised under different circumstance.