Hybrid modeling for carbon monoxide gas-phase catalytic coupling to synthesize dimethyl oxalate process  

作　　者：Shida Gao Cuimei Bo Chao Jiang Quanling Zhang Genke Yang Jian Chu 

机构地区：[1]Ningbo Artificial Intelligence Institute,Shanghai Jiao Tong University,Ningbo 315012,China [2]Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China [3]Key Laboratory of Industrial Internet+Safety Production of Hazardous Chemical,Ministry of Emergency Management,Nanjing Tech University,Nanjing 211816,China

出　　处：《Chinese Journal of Chemical Engineering》2024年第6期234-250,共17页中国化学工程学报（英文版）

基　　金：supported in part by the National Key Research and Development Program of China(2022YFB3305300);the National Natural Science Foundation of China(62173178).

摘　　要：Ethylene glycol(EG)plays a pivotal role as a primary raw material in the polyester industry,and the syngas-to-EG route has become a significant technical route in production.The carbon monoxide(CO)gas-phase catalytic coupling to synthesize dimethyl oxalate(DMO)is a crucial process in the syngas-to-EG route,whereby the composition of the reactor outlet exerts influence on the ultimate quality of the EG product and the energy consumption during the subsequent separation process.However,measuring product quality in real time or establishing accurate dynamic mechanism models is challenging.To effectively model the DMO synthesis process,this study proposes a hybrid modeling strategy that integrates process mechanisms and data-driven approaches.The CO gas-phase catalytic coupling mechanism model is developed based on intrinsic kinetics and material balance,while a long short-term memory(LSTM)neural network is employed to predict the macroscopic reaction rate by leveraging temporal relationships derived from archived measurements.The proposed model is trained semi-supervised to accommodate limited-label data scenarios,leveraging historical data.By integrating these predictions with the mechanism model,the hybrid modeling approach provides reliable and interpretable forecasts of mass fractions.Empirical investigations unequivocally validate the superiority of the proposed hybrid modeling approach over conventional data-driven models(DDMs)and other hybrid modeling techniques.

关 键 词：Carbon monoxide Dynamic modeling Hybrid model Reaction kinetics Semi-supervised learning 

分 类 号：TQ203.2[化学工程—有机化工] TQ225.24TQ223.162