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Machine learning-assisted discovery of flow reactor designs

Engineering and Technology

Machine learning-assisted discovery of flow reactor designs

T. Savage, N. Basha, et al.

Discover how a team of researchers, including Tom Savage, Nausheen Basha, and Omar Matar, employs machine learning and additive manufacturing to revolutionize chemical reactor design. Their innovative approach led to a remarkable 60% improvement in plug flow performance, showcasing the synergy between advanced manufacturing and augmented intelligence.

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Playback language: English
Abstract
Additive manufacturing allows for creating complex reactor geometries, expanding design spaces. This paper presents a machine learning-assisted approach for designing chemical reactors, using high-dimensional parameterizations, computational fluid dynamics (CFD), and multi-fidelity Bayesian optimization. The approach focuses on coiled reactors, identifying design features leading to ~60% improved plug flow performance compared to conventional designs. This demonstrates the potential of combining advanced manufacturing with augmented intelligence for enhanced reactor performance.
Publisher
Nature Chemical Engineering
Published On
Aug 05, 2024
Authors
Tom Savage, Nausheen Basha, Jonathan McDonough, James Krassowski, Omar Matar, Ehecatl Antonio del Rio Chanona
Tags
additive manufacturing
chemical reactors
machine learning
Bayesian optimization
plug flow performance
CFD
design features

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