A machine learning‐driven pore‐scale network model coupling reaction kinetics and interparticle transport for catalytic process design
Author(s)
Type
Journal Article
Abstract
A pore-scale dual-network model is presented with kinetics (DNMK), enhanced by machine learning (ML), for efficient multiscale modeling of reaction-transport coupled catalytic processes in porous systems. In such systems, apparent catalytic performance arises from the intricate interplay between intrinsic microkinetics and inter-particle transport phenomena. By explicitly resolving these coupled effects, DNMK provides mechanistic insight into how spatial particle arrangements and transport limitations govern overall reactor performance. A key innovation of this work is the integration of ML-based surrogates to accelerate the microkinetic module, effectively bridging the large spatial and temporal scale mismatches between transport and catalytic reactions. This hybrid approach achieves up to a 750-fold computational speed-up while preserving full physical and chemical fidelity. The framework is demonstrated for sorption-enhanced CO2 hydrogenation to methanol, where DNMK identifies optimal catalyst-sorbent configurations that maximize apparent activity and reactor-scale performance. More broadly, DNMK establishes a high-resolution, ML-driven platform for digital catalytic experimentation, enabling predictive, in silico optimization of catalyst scaling, utilization, and process intensification. By allowing rapid, physically consistent evaluation of complex catalytic systems, DNMK reduces reliance on costly experimental trials and opens new pathways for data-driven reactor and process design across diverse chemical engineering applications.
Date Issued
2026-02-09
Date Acceptance
2025-12-01
Citation
Advanced Science, 2026, 13 (8)
ISSN
2198-3844
Publisher
Wiley
Journal / Book Title
Advanced Science
Volume
13
Issue
8
Copyright Statement
© 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41332317
Subjects
CO2 hydrogenation
Catalytic processes
Kinetics
Machine learning
Pore‐scale modeling
Porous media
Publication Status
Published
Coverage Spatial
Germany
Article Number
e13649
Date Publish Online
2025-12-03