An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

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外文摘要：To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention-convolution-gate recurrent unit(LACG)architecture with three sub-modules-a basic module,a brand-new light attention module,and a residue module-that are specially designed to learn the gen-eral dynamic behavior,transient disturbances,and other input factors of chemical processes,respec-tively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the dis-charge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowl-edge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

外文关键词：

Interpretable machine learningLight attention-convolution-gate recurrent unit architectureProcess knowledge discoveryData-driven process modelIntelligent manufacturing

作者：

Yue Li、Ning Li、Jingzheng Ren、Weifeng Shen

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作者单位：

School of Chemistry and Chemical Engineering,Chongqing University,Chongqing 400044,China

Chongqing Key Laboratory of Catalysis and New Environmental Materials,School of Environment and Resources,Chongqing Technology and Business University,Chongqing 400067,China

Department of Industrial and Systems Engineering,The Hong Kong Polytechnic University,Hong Kong 999077,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaChongqing Science Fund for Distinguished Young ScholarsFundamental Research Funds for the Central Universities

项目编号：

221228022227804421878028CSTB2022NSCQ-JQX00212022CDJXY-003

出版年：

2024

DOI：

10.1016/j.eng.2024.07.009

工程(英文)

CSTPCDEI

ISSN：2095-8099

年,卷(期)：2024.39(8)