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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampHerrera Agudelo, William Eduardo-
dc.contributor.authorunicampMaciel Filho, Rubens-
dc.typeArtigopt_BR
dc.titleProduct quality monitoring using extreme learning machines and bat algorithms: a case study in second-generation ethanol productionpt_BR
dc.contributor.authorFarias Jr., Felix S.-
dc.contributor.authorAzevedo, Renan A.-
dc.contributor.authorRivera, Elmer C.-
dc.contributor.authorHerrera, William E.-
dc.contributor.authorMaciel Filho, Rubens-
dc.contributor.authorLima Jr., Luiz P.-
dc.subjectEtanolpt_BR
dc.subject.otherlanguageEthanolpt_BR
dc.description.abstractIn this study, a new methodology for online monitoring of second-generation ethanol production is presented. The prediction of the concentration of ethanol, substrate and cells from secondary measurements (pH, turbidity, CO2 and temperature) is compared with experimental data from the fermentation of a mixture of molasses and hydrolyzed sugarcane bagasse from the alkaline hydrogen peroxide pre-treatment at 25 % and 75 % of volume. The Extreme Learning Machine algorithm (ELM) provided a very good alternative to traditional Multilayer Perceptron neural networks (MLP) and the BAT optimization technique applied to ELM algorithm provided a fast parallel search for the best solution. This new methodology offered a good alternative to the standard soft- sensor approach based on MLP and fast and reliable product quality estimates for key process variables as in second-generation ethanol productionpt_BR
dc.relation.ispartofComputer - aided chemical engineeringpt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2014-
dc.language.isoengpt_BR
dc.description.volume33pt_BR
dc.description.firstpage955pt_BR
dc.description.lastpage960pt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn1570-7946pt_BR
dc.identifier.eissn2543-1331pt_BR
dc.identifier.doi10.1016/B978-0-444-63456-6.50160-5pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/B9780444634566501605pt_BR
dc.date.available2020-10-07T23:11:35Z-
dc.date.accessioned2020-10-07T23:11:35Z-
dc.description.provenanceSubmitted by Sanches Olivia (olivias@unicamp.br) on 2020-10-07T23:11:35Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-02-11T21:12:09Z : No. of bitstreams: 1 2-s2.0-84902961691.pdf: 5144284 bytes, checksum: 1ed9a78dd02b48b5719acb73f57416de (MD5)en
dc.description.provenanceMade available in DSpace on 2020-10-07T23:11:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2014en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/350720-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Desenvolvimento de Processos e Produtospt_BR
dc.contributor.unidadeFaculdade de Engenharia Químicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Químicapt_BR
dc.subject.keywordExtreme learning machinept_BR
dc.subject.keywordArtificial neural networkspt_BR
dc.subject.keywordBAT algorithmpt_BR
dc.identifier.source2-s2.0-84902961691pt_BR
dc.creator.orcidsem informaçãopt_BR
dc.creator.orcid0000-0001-6511-7283pt_BR
dc.type.formArtigopt_BR
dc.description.otherSponsorshipsem informaçãopt_BR
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