The automated discovery of kinetic rate models – methodological frameworks-Reference-Cited by-同舟云学术

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The automated discovery of kinetic rate models – methodological frameworks

Published:2024 Issue:5 Volume:3 Page:954-968
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

de Carvalho Servia Miguel Ángel¹^ORCID,Sandoval Ilya Orson¹^ORCID,Hii King Kuok (Mimi)²^ORCID,Hellgardt Klaus¹^ORCID,Zhang Dongda³^ORCID,Antonio del Rio Chanona Ehecatl¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK

2. Department of Chemistry, Imperial College London, White City, London, W12 0BZ, UK

3. Department of Chemical Engineering, the University of Manchester, Manchester, M13 9PL, UK

Abstract

Two automated knowledge discovery methodologies (ADoK-S & ADoK-W) are created whereby symbolic regression, parameter estimation, information criteria and model-based design of experiments synergize for the optimized discovery of kinetic rate models.

Funder

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00212H

Reference50 articles.

1. Modelling the scaling properties of human mobility

2. The scaling laws of human travel

3. A Mathematical Framework for Modelling the Metastatic Spread of Cancer

4. Kinetics of Front-End Acetylene Hydrogenation in Ethylene Production

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