Artigo De: orcid

Automation of Data-Driven Rate Equation Screening for Heterogeneously Catalyzed Reactions

Industrial & Engineering Chemistry Research

Vasco A. C. Saltão; Joris W. Thybaut; Pedro S. F. Mendes2022ACS Publications

Informações chave

Autores:

Vasco A. C. Saltão (Vasco António Correia Saltão); Joris W. Thybaut; Laura Pirro; Filipe G. Freire (Filipe José Da Cunha Monteiro Gama Freire); Pedro S. F. Mendes (Pedro Simão Freitas Mendes)

Publicado em

09/09/2022

Resumo

Automating the generation of suitable kinetic models could dramatically improve its application to novel reactions. Therefore, a software tool was developed to automatically propose rate equations for a catalytic reaction, purely based on experimental data. The tool screens initial rate equations (from a comprehensive, theoretical, physically meaningful library) by comparing their predicted trends with those present in the experimental data, thereby eliminating the rate equations that cannot reproduce the trends in the data. Afterward, the feasible rate equations are ranked based on trend similarity, resulting in an ordered list of rate equations ready for regression. For most of the tested literature datasets, the tool proposed the same rate equation as experienced researchers. This is a key first step into the automation of kinetic modeling that, once generalized, will allow its widespread use in the understanding of catalytic reactions.

Detalhes da publicação

Autores da comunidade :

Versão da publicação

NA - Versão desconhecida

Editora

ACS Publications

Ligação para a versão da editora

https://pubs.acs.org/doi/10.1021/acs.iecr.2c01920

Título do contentor da publicação

Industrial & Engineering Chemistry Research

Primeira página ou número de artigo

13841

Última página

13853

Volume

61

Fascículo

37

ISSN

0888-5885

GovDoc

Domínio Científico (FOS)

chemical-sciences - Química

Palavras-chave

  • Heterogeneous Catalysis
  • Chemical Engineering

Idioma da publicação (código ISO)

eng - Inglês

Identificador alternativo (URI)

https://doi.org/10.1021/acs.iecr.2c01920

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