文献类型：期刊文献

英文题名：Explainable AI-driven discovery of optimal modular architectures of CO2 reduction reactor clusters

作者：Fu, Kaihao[1,4];Li, Xinyuan[3];Li, Ping[1];Guo, Wenze[3];Cao, Chenxi[2,3];He, Wangli[3];Du, Wenli[3];Qian, Feng[3]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, Key Lab Green Chem Engn & Ind Catalysis, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[4]Suzhou Natl Lab, Suzhou 215123, Peoples R China

年份：2026

卷号：20

期号：8

外文期刊名：ENGINEERING CHEMICAL ENGINEERING

收录：;EI(收录号：20262420913588);WOS:【SCI-EXPANDED(收录号:WOS:001791293600002)】；

基金：This work was supported by the National Key Research and Development Program of China (2022YFB3305900), the National Natural Science Foundation of China (22441040), Shanghai Pilot Program for Basic Research (22TQ1400100-3) and the Natural Science Foundation of Shanghai (24ZR1414900).

语种：英文

外文关键词：modular devices; scale-up; structure-performance relationship; machine learning

摘要：The performance degradation of modular devices during scaling up necessitates rational design of the integration structure. However, its complex structure makes it challenging to reveal the mechanism of the effect of hierarchical multi-scale structural parameters on performance. This study proposes a data-driven framework to analyze structure-performance relationships and identify optimal scale-up patterns, using a CO2 reduction microreactor as a case study. A quantitative relationship between structure and performance is established using extreme gradient boosting tree combined with the Shapley additive explanations analysis, elucidating the regulatory mechanisms of structural parameters on performance. While a classification model is utilized to define the criteria for identifying optimal structures. Additionally, optimal scale-up design patterns under various scenarios are uncovered using K-means clustering. The results indicate that Small-sized few-stack parallel structures and large-sized single-stack structures s are the scaling-up patterns that can balance cost and performance. This approach provides important insights for the industrial scale design of modular devices.