Development of a surrogate artificial neural network for microkinetic modeling: case study with methanol synthesis

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dc.creatorBruno Lacerda de Oliveira Campos
dc.creatorAndréa Oliveira Souza da Costa
dc.creatorKarla Herrera Delgado
dc.creatorStephan Pitter
dc.creatorJörg Sauer
dc.creatorEsly Ferreira da Costa Junior
dc.date.accessioned2026-01-22T19:56:15Z
dc.date.issued2024-01-15
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipOutra Agência
dc.identifier.doihttps://doi.org/10.1039/D3RE00409K
dc.identifier.issn2058-9883
dc.identifier.urihttps://hdl.handle.net/1843/1475
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofReaction Chemistry & Engineering
dc.rightsAcesso aberto
dc.rightsAttribution 3.0 Brazilen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/br/
dc.subjectRede neural artificial
dc.subjectModelo microcinético
dc.subjectMicrocinética
dc.subjectSíntese de metanol
dc.titleDevelopment of a surrogate artificial neural network for microkinetic modeling: case study with methanol synthesis
dc.typeArtigo de periódico
local.citation.epage1060
local.citation.issue5
local.citation.spage1047
local.citation.volume9
local.description.resumoMicrokinetic models allow the description of complex reaction kinetics but require high computational costs, hindering their combination with detailed reactor models. In this contribution, a methodology to develop a surrogate artificial neural network (ANN) was proposed and demonstrated for methanol synthesis on Cu/Znbased catalysts. The resulting model accurately reproduces the simulations of the original microkinetic model, reducing the computational costs by orders of magnitude. In the developed methodology, the ANN learns only the kinetics of the global reaction rates, thereby decreasing model complexity and computational costs while ensuring thermodynamic consistency. In addition, an improved activation function for the ANN was designed in this work to minimize computational costs and to smooth out calculations. The proposed approach creates a bridge to integrate microkinetics into applications in the field of reaction engineering, such as reactor design, processoptimization, andscale-up.
local.identifier.orcidhttps://orcid.org/0000-0002-1820-5173
local.identifier.orcidhttps://orcid.org/0000-0002-6763-9752
local.identifier.orcidhttps://orcid.org/0000-0003-1889-3719
local.identifier.orcidhttps://orcid.org/0000-0003-1815-6364
local.identifier.orcidhttps://orcid.org/0000-0003-3133-4110
local.identifier.orcidhttps://orcid.org/0000-0002-9245-4223
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA QUÍMICA
local.publisher.initialsUFMG
local.subject.cnpqENGENHARIAS::ENGENHARIA QUIMICA
local.url.externahttps://pubs.rsc.org/en/content/articlelanding/2024/re/d3re00409k

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