From black box to machine learning

Detalhes bibliográficos
Autor(a) principal: Galinha, Claudia F.
Data de Publicação: 2021
Outros Autores: Crespo, João G.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134696
Resumo: UIDB/50006/2020 UIDP/50006/2020
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spelling From black box to machine learningA journey through membrane process modellingANNArtificial intelligenceBig dataChemometricsFluorescence excitation-emission matrices (EEM)Membrane processesModellingMultivariate data analysisPCAPLSChemical Engineering (miscellaneous)Process Chemistry and TechnologyFiltration and SeparationUIDB/50006/2020 UIDP/50006/2020Membrane processes are complex systems, often comprising several physicochemical phenomena, as well as biological reactions, depending on the systems studied. Therefore, process modelling is a requirement to simulate (and predict) process and membrane performance, to infer about optimal process conditions, to assess fouling development, and ultimately, for process monitoring and control. Despite the actual dissemination of terms such as Machine Learning, the use of such computational tools to model membrane processes was regarded by many in the past as not useful from a scientific point-of-view, not contributing to the understanding of the phenomena involved. Despite the controversy, in the last 25 years, data driven, non-mechanistic modelling is being applied to describe different membrane processes and in the development of new modelling and monitoring approaches. Thus, this work aims at providing a personal perspective of the use of non-mechanistic modelling in membrane processes, reviewing the evolution supported in our own experience, gained as research group working in the field of membrane processes. Additionally, some guidelines are provided for the application of advanced mathematical tools to model membrane processes.LAQV@REQUIMTEDQ - Departamento de QuímicaRUNGalinha, Claudia F.Crespo, João G.2022-03-16T23:23:45Z2021-082021-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/134696eng0076-6356PURE: 42220903https://doi.org/10.3390/membranes11080574info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:13:10Zoai:run.unl.pt:10362/134696Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:12.485591Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv From black box to machine learning
A journey through membrane process modelling
title From black box to machine learning
spellingShingle From black box to machine learning
Galinha, Claudia F.
ANN
Artificial intelligence
Big data
Chemometrics
Fluorescence excitation-emission matrices (EEM)
Membrane processes
Modelling
Multivariate data analysis
PCA
PLS
Chemical Engineering (miscellaneous)
Process Chemistry and Technology
Filtration and Separation
title_short From black box to machine learning
title_full From black box to machine learning
title_fullStr From black box to machine learning
title_full_unstemmed From black box to machine learning
title_sort From black box to machine learning
author Galinha, Claudia F.
author_facet Galinha, Claudia F.
Crespo, João G.
author_role author
author2 Crespo, João G.
author2_role author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Galinha, Claudia F.
Crespo, João G.
dc.subject.por.fl_str_mv ANN
Artificial intelligence
Big data
Chemometrics
Fluorescence excitation-emission matrices (EEM)
Membrane processes
Modelling
Multivariate data analysis
PCA
PLS
Chemical Engineering (miscellaneous)
Process Chemistry and Technology
Filtration and Separation
topic ANN
Artificial intelligence
Big data
Chemometrics
Fluorescence excitation-emission matrices (EEM)
Membrane processes
Modelling
Multivariate data analysis
PCA
PLS
Chemical Engineering (miscellaneous)
Process Chemistry and Technology
Filtration and Separation
description UIDB/50006/2020 UIDP/50006/2020
publishDate 2021
dc.date.none.fl_str_mv 2021-08
2021-08-01T00:00:00Z
2022-03-16T23:23:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/134696
url http://hdl.handle.net/10362/134696
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0076-6356
PURE: 42220903
https://doi.org/10.3390/membranes11080574
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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