Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Fonseca, Rafael Frederico
Orientador(a): Kwong, Wu Hong lattes
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de São Carlos
Câmpus São Carlos
Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Química - PPGEQ
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/7986
Resumo: Solid-state fermentation is characterized by the growth of microorganisms in absence of free water. In one hand, it is advantageous because simulates their natural environment, enabling the use of agro industrial residues in natura. On the other hand, it limits heat transfer between elements, restricting control over the temperature of the medium. In fact, the microbial growth and the product formation dynamics are directly affected by the environmental conditions and variations can be harmful to the process productivity. As a consequence, the temperature increase caused by metabolic heat needs to be avoided. Studies concerning the microbial dynamics dealing with these variations are scarce. Moreover, it was not found any control laws, with guarantee of stability, which was designed for a reference tracking and to minimize the disturbances effects. Thus, two fronts need to be addressed for the solid-state fermentation viability: the development of a mathematical model able to estimate the effects of environmental changes in the process; and a temperature control system able to handle the heat from microbial metabolism. The model was used in a computational algorithm in order to determine if there was a temperature profile that would be more favorable to the products formation. In this work two control laws were studied, a proportional integrative, because it is the most widespread in the industry, and a model base predictive controller, because of its multivariable control versatility. Both control laws were simulated and then implemented in an eleven liters agitated drum bioreactor. Some of the various methods for PI controller parameters settings had their performance and relative stability requirement evaluated. The one that was proved stable was implemented in the bioreactor. Due to the uncertainties of the fermentation process, a self-adjustment mechanism was added to the predictive controller, in spite of the developed mathematical model, in order to avoid some estimation mistakes caused by some non-estimated states of the real process. The controller achieved an adequate performance with this approach. The results showed that the microorganisms were more efficient at a constant 32°C temperature. In addition, both developed controllers presented appropriate results facing the fermentation process requirement, with mean deviances from the referential temperature below 0,6°C and a maximum error of 2,8°C.
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spelling Fonseca, Rafael FredericoKwong, Wu Honghttp://lattes.cnpq.br/2034426284174731http://lattes.cnpq.br/2586664275994045105eaf69-b8d4-43b6-a5ab-9e43041682272016-10-20T18:16:05Z2016-10-20T18:16:05Z2016-05-04FONSECA, Rafael Frederico. Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial. 2016. Tese (Doutorado em Engenharia Química) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/20.500.14289/7986.https://repositorio.ufscar.br/handle/20.500.14289/7986Solid-state fermentation is characterized by the growth of microorganisms in absence of free water. In one hand, it is advantageous because simulates their natural environment, enabling the use of agro industrial residues in natura. On the other hand, it limits heat transfer between elements, restricting control over the temperature of the medium. In fact, the microbial growth and the product formation dynamics are directly affected by the environmental conditions and variations can be harmful to the process productivity. As a consequence, the temperature increase caused by metabolic heat needs to be avoided. Studies concerning the microbial dynamics dealing with these variations are scarce. Moreover, it was not found any control laws, with guarantee of stability, which was designed for a reference tracking and to minimize the disturbances effects. Thus, two fronts need to be addressed for the solid-state fermentation viability: the development of a mathematical model able to estimate the effects of environmental changes in the process; and a temperature control system able to handle the heat from microbial metabolism. The model was used in a computational algorithm in order to determine if there was a temperature profile that would be more favorable to the products formation. In this work two control laws were studied, a proportional integrative, because it is the most widespread in the industry, and a model base predictive controller, because of its multivariable control versatility. Both control laws were simulated and then implemented in an eleven liters agitated drum bioreactor. Some of the various methods for PI controller parameters settings had their performance and relative stability requirement evaluated. The one that was proved stable was implemented in the bioreactor. Due to the uncertainties of the fermentation process, a self-adjustment mechanism was added to the predictive controller, in spite of the developed mathematical model, in order to avoid some estimation mistakes caused by some non-estimated states of the real process. The controller achieved an adequate performance with this approach. The results showed that the microorganisms were more efficient at a constant 32°C temperature. In addition, both developed controllers presented appropriate results facing the fermentation process requirement, with mean deviances from the referential temperature below 0,6°C and a maximum error of 2,8°C.Uma das características da fermentação em estado sólido é que ela ocorre na ausência de água livre. Isso a torna vantajosa por simular o ambiente natural dos microrganismos com possibilidade de uso de resíduos agroindustriais in natura. Por outro lado, dificulta a transferência de calor entre os elementos do processo e, com isso, a capacidade de controlar a temperatura do meio fica debilitada. Por sua vez, a dinâmica do crescimento microbiano e a formação de produtos de interesse estão diretamente relacionadas às condições ambientais, cujas variações podem ser prejudiciais à produtividade do processo. Ao mesmo tempo, o calor gerado pelo metabolismo microbiano aumenta a temperatura do processo, que necessita ser regulada. Estudos que revelam como os microrganismos se comportam frente essas variações são escassos. Além disso, não foram encontradas leis de controle para a FES, com garantia de estabilidade, a fim de se minimizar os efeitos dos distúrbios. Duas frentes se destacam para a viabilização da FES: o desenvolvimento de um modelo matemático capaz de estimar os efeitos das variações das condições ambientais na dinâmica do processo e um sistema de controle da temperatura apropriado para lidar com os distúrbios gerados pelo crescimento celular. Em conjunto com a modelagem matemática, foram empregados mecanismos computacionais para averiguar qual seria o perfil de temperaturas que mais favorece à formação dos produtos. Por sua vez, foram estudados dois tipos de controladores: os proporcionais integrativos, pela ampla aplicação industrial, e os preditivos baseados em modelo, pela versatilidade no controle multivariável. Os sistemas de controle foram testados em um biorreator de 11 litros de volume nominal. Dentre várias, algumas metodologias para ajuste dos controladores proporcionais integrativos foram avaliadas nos quesitos desempenho e estabilidade relativa durante a fase de simulações. A metodologia que se provou estável nos testes realizados foi implementada no biorreator. Já para o controlador preditivo, frente às incertezas do processo fermentativo, foi necessário desenvolver um mecanismo de auto ajuste do modelo desenvolvido, a fim de que os erros dos estados não estimados do processo real fossem compensados e o controlador tivesse um desempenho adequado. Os resultados mostraram que o microrganismo, Aspergilus niger 3T5B8, produz uma quantidade maior de metabólitos de interesse a uma temperatura constante de 32°C. Além disso, ambos controladores utilizados apresentaram resultados apropriados aos requisitos do processo fermentativo, ou seja, com desvio médio da temperatura de referência menor do que 0,6°C.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)porUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Engenharia Química - PPGEQUFSCarFermentação em estado sólidoControle proporcional integrativoControle preditivoModelagem matemáticaOtimização do processoSolid state fermentationProportional integrative controlPredictive controlMathematical modelingProcess optimizationENGENHARIAS::ENGENHARIA QUIMICADinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorialinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisOnline6006001b438967-d223-4579-b590-3fdf71b98456info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALTeseRFF.pdfTeseRFF.pdfapplication/pdf11255323https://repositorio.ufscar.br/bitstreams/82bd1247-1dfc-498f-9880-135cca93ffac/download6490de6218937c15e5bbdc6d9672a300MD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-81957https://repositorio.ufscar.br/bitstreams/b4a57602-9b54-48a3-9dba-f1cc781e3a0b/downloadae0398b6f8b235e40ad82cba6c50031dMD52falseAnonymousREADTEXTTeseRFF.pdf.txtTeseRFF.pdf.txtExtracted texttext/plain283688https://repositorio.ufscar.br/bitstreams/61066149-7a82-4ffc-a331-670f0a409e8c/downloadf8291b4e65662fbbd120f134465c9718MD55falseAnonymousREADTHUMBNAILTeseRFF.pdf.jpgTeseRFF.pdf.jpgIM Thumbnailimage/jpeg7295https://repositorio.ufscar.br/bitstreams/7d62ae35-40aa-498a-8d18-3e1c32b2020f/download8a5743f753a06d75f5d977592e745e8dMD56falseAnonymousREAD20.500.14289/79862025-02-05 18:53:29.601Acesso abertoopen.accessoai:repositorio.ufscar.br:20.500.14289/7986https://repositorio.ufscar.brRepositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestrepositorio.sibi@ufscar.bropendoar:43222025-02-05T21:53:29Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)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
dc.title.por.fl_str_mv Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
title Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
spellingShingle Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
Fonseca, Rafael Frederico
Fermentação em estado sólido
Controle proporcional integrativo
Controle preditivo
Modelagem matemática
Otimização do processo
Solid state fermentation
Proportional integrative control
Predictive control
Mathematical modeling
Process optimization
ENGENHARIAS::ENGENHARIA QUIMICA
title_short Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
title_full Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
title_fullStr Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
title_full_unstemmed Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
title_sort Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial
author Fonseca, Rafael Frederico
author_facet Fonseca, Rafael Frederico
author_role author
dc.contributor.authorlattes.por.fl_str_mv http://lattes.cnpq.br/2586664275994045
dc.contributor.author.fl_str_mv Fonseca, Rafael Frederico
dc.contributor.advisor1.fl_str_mv Kwong, Wu Hong
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/2034426284174731
dc.contributor.authorID.fl_str_mv 105eaf69-b8d4-43b6-a5ab-9e4304168227
contributor_str_mv Kwong, Wu Hong
dc.subject.por.fl_str_mv Fermentação em estado sólido
Controle proporcional integrativo
Controle preditivo
Modelagem matemática
Otimização do processo
topic Fermentação em estado sólido
Controle proporcional integrativo
Controle preditivo
Modelagem matemática
Otimização do processo
Solid state fermentation
Proportional integrative control
Predictive control
Mathematical modeling
Process optimization
ENGENHARIAS::ENGENHARIA QUIMICA
dc.subject.eng.fl_str_mv Solid state fermentation
Proportional integrative control
Predictive control
Mathematical modeling
Process optimization
dc.subject.cnpq.fl_str_mv ENGENHARIAS::ENGENHARIA QUIMICA
description Solid-state fermentation is characterized by the growth of microorganisms in absence of free water. In one hand, it is advantageous because simulates their natural environment, enabling the use of agro industrial residues in natura. On the other hand, it limits heat transfer between elements, restricting control over the temperature of the medium. In fact, the microbial growth and the product formation dynamics are directly affected by the environmental conditions and variations can be harmful to the process productivity. As a consequence, the temperature increase caused by metabolic heat needs to be avoided. Studies concerning the microbial dynamics dealing with these variations are scarce. Moreover, it was not found any control laws, with guarantee of stability, which was designed for a reference tracking and to minimize the disturbances effects. Thus, two fronts need to be addressed for the solid-state fermentation viability: the development of a mathematical model able to estimate the effects of environmental changes in the process; and a temperature control system able to handle the heat from microbial metabolism. The model was used in a computational algorithm in order to determine if there was a temperature profile that would be more favorable to the products formation. In this work two control laws were studied, a proportional integrative, because it is the most widespread in the industry, and a model base predictive controller, because of its multivariable control versatility. Both control laws were simulated and then implemented in an eleven liters agitated drum bioreactor. Some of the various methods for PI controller parameters settings had their performance and relative stability requirement evaluated. The one that was proved stable was implemented in the bioreactor. Due to the uncertainties of the fermentation process, a self-adjustment mechanism was added to the predictive controller, in spite of the developed mathematical model, in order to avoid some estimation mistakes caused by some non-estimated states of the real process. The controller achieved an adequate performance with this approach. The results showed that the microorganisms were more efficient at a constant 32°C temperature. In addition, both developed controllers presented appropriate results facing the fermentation process requirement, with mean deviances from the referential temperature below 0,6°C and a maximum error of 2,8°C.
publishDate 2016
dc.date.accessioned.fl_str_mv 2016-10-20T18:16:05Z
dc.date.available.fl_str_mv 2016-10-20T18:16:05Z
dc.date.issued.fl_str_mv 2016-05-04
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dc.identifier.citation.fl_str_mv FONSECA, Rafael Frederico. Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial. 2016. Tese (Doutorado em Engenharia Química) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/20.500.14289/7986.
dc.identifier.uri.fl_str_mv https://repositorio.ufscar.br/handle/20.500.14289/7986
identifier_str_mv FONSECA, Rafael Frederico. Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial. 2016. Tese (Doutorado em Engenharia Química) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/20.500.14289/7986.
url https://repositorio.ufscar.br/handle/20.500.14289/7986
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Câmpus São Carlos
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publisher.none.fl_str_mv Universidade Federal de São Carlos
Câmpus São Carlos
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