Explainable AI-driven discovery of optimal modular architectures of CO₂ reduction reactor clusters

Abstract

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.

Graphical abstract

Keywords

modular devices / scale-up / structure-performance relationship / machine learning

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