From black box to machine learning
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138083337142272 |