Scale-up of complex molecular reaction system by hybrid mechanistic modeling and deep transfer learning

Zhengyu Chen, Yongqing Xie, Chunming Xu and Linzhou Zhang ()
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Zhengyu Chen: China University of Petroleum
Yongqing Xie: China University of Petroleum
Chunming Xu: China University of Petroleum
Linzhou Zhang: China University of Petroleum

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract The scale-up of chemical processes involves substantial changes in reactor size, operational modes, and data characteristics, leading to significant challenges in predicting product distribution across scales. This study presents a unified modeling framework that integrates the mechanistic model with deep transfer learning to accelerate chemical process scale-up. The framework is demonstrated through a case study on naphtha fluid catalytic cracking. A molecular-level kinetic model was developed from laboratory-scale experimental data, and a deep neural network was designed and trained to represent complex molecular reaction systems. To address the challenge of discrepancies in data types at various scales, a property-informed transfer learning strategy was developed by incorporating bulk property equations into the neural network. This approach enabled automated prediction of pilot-scale product distribution with minimal data. Moreover, process conditions of the pilot plant were optimized using a multi-objective optimization algorithm.

Date: 2025
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DOI: 10.1038/s41467-025-63982-2

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