Work

Modelling of industrial packed bed reactors: connecting CFD with imaging and AI

Year: 2026

Type: review

Abstract: Packed bed reactors are pivotal for the energy transition, yet their industrial design and scale-up remain constrained by the wide separation of length and time scales governing transport, reaction and phase behaviour. While high-fidelity pore-scale models, such as particle-resolved computational fluid dynamics (PRCFD), offer unprecedented insights, their high computational cost makes them impractical for industrial design. Consequently, design of large-scale packed bed reactors still relies on empirical correlations that oversimplify multiphase and reactive transport, often resulting in overdesign associated with elevated costs and increased emissions. A substantial gap exists between PRCFD studies of idealised packed beds and the complex environment of real industrial reactors. This perspective article explores the recent trends in PRCFD and positions it in the rapidly evolving modelling domain for packed bed reactors. We further highlight the recent innovations in Reduced Order Modelling (numerical homogenisation, pore network modelling), artificial intelligence and non-invasive imaging techniques, and their integration with PRCFD.

Source: Current Opinion in Chemical Engineering

DOI: 10.1016/j.coche.2026.101247

Authors: Shashank S Tiwari, Racha V Kumar, Balivada K Kumar, Steffen Berg, Himanshu Goyal

Institutions: Shell (India), Indian Institute of Technology Madras, Norwegian University of Science and Technology, PoreLab, Shell (Netherlands)

Language: English

Topic: Heat and Mass Transfer in Porous Media

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,250