Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/14844 |
Resumo: | Enzymatic reactions of esterification of fatty acids with carbohydrates generate biosurfactants, products with high capacity to reduce surface and interfacial tensions, applicable mainly in the food, pharmaceutical and cosmetic industries. Mathematical modeling, in turn, can be a useful tool, in its different approaches, for the simulation and optimization of enzymatic processes. Thus, this work was carried out in three distinct steps and aimed at the mathematical modeling of enzymatic processes to produce biosurfactants, making use of the application of phenomenological (semi-mechanistic), neural and fuzzy approaches. The phenomenological kinetic model of Ping Pong Bi Bi was fitted to experimental data. For this, kinetic data of the production of biosurfactants by esterification of oleic and lauric acids with fructose and lactose, using immobilized lipase B from Candida antarctica (CALB-IM-T2-350) and lipase from Pseudomonas fluorescens (PFL) immobilized on octyl-silica (silanized with octyltriethoxysilane), provided by LabEnz-UFSCar, were used. The classic Levenberg-Marquardt parameter fitting method was applied, resulting in a good correspondence between the proposed model and the experimental data. For validation of the semi-mechanistic model, a new set of experimental data was used, showing excellent predictive capacity of the model. Then, neural kinetic models were built using experimental data of enzymatic esterification of xylose with oleic and/or lauric acids, performed using the biocatalyst CALB-IM-T2-350 and CALB derivatives immobilized on Silica Magnetic Microparticles (SMMPs) with octyl groups (CALB-SMMP-octyl) or with octyl groups plus glutaraldehyde (CALB-SMMP-octyl-glu). Using Matlab Neural Network Toolbox, five artificial neural networks (ANNs) were trained, one for each type of biocatalyst and acid, obtaining R-squared values greater than 0.97. As a last effort in neural modeling, two ANNs were fitted (for two of the biocatalysts), each one of them already incorporating, in its inputs, an option referring to the type of acid. The R-squared values, above 0.98, also showed good predictability. Finally, modeling by fuzzy inference systems was studied, using the Neuro Fuzzy Designer tool from ANFIS (Adaptive Network-Based Fuzzy Inference System) of Matlab. Fuzzy models were built for each of the three biocatalysts under study (CALB-IM-T2-350, CALB-SMMP-octyl and CALB-SMMP-octyl-glu), considering as input linguistic variables the type of acid, temperature, reaction time and substrate molar ratio to predict the conversion of the esterification process. Gaussian membership functions and linear output functions were used, in a Takagi- Sugeno’s fuzzy approach. The parameters were fitted by a hybrid parametric optimization method. The results showed that the fuzzy model outputs were very close to the targets, with RMSE values below 0.006. To demonstrate the potential of fuzzy modeling to optimize processes, response surfaces were built for the conversion of xylose as a function of different operating conditions. The fuzzy surfaces indicated that higher values of conversion are reached after 45h of reaction, at temperatures above 50°C and at RMS of 1:0.2 (acid:sugar). Thus, the present work explored, in a broad way, all the capability of mathematical modeling, under different approaches, in the study of enzymatic production of biosurfactants. |
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Torres, Alice de Carvalho LimaSousa Júnior, Ruy dehttp://lattes.cnpq.br/1983482879541203http://lattes.cnpq.br/3634638433614642a74bd895-72a4-4664-a586-3e89613907702021-09-10T12:40:53Z2021-09-10T12:40:53Z2021-07-08TORRES, Alice de Carvalho Lima. Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas. 2021. Tese (Doutorado em Engenharia Química) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14844.https://repositorio.ufscar.br/handle/ufscar/14844Enzymatic reactions of esterification of fatty acids with carbohydrates generate biosurfactants, products with high capacity to reduce surface and interfacial tensions, applicable mainly in the food, pharmaceutical and cosmetic industries. Mathematical modeling, in turn, can be a useful tool, in its different approaches, for the simulation and optimization of enzymatic processes. Thus, this work was carried out in three distinct steps and aimed at the mathematical modeling of enzymatic processes to produce biosurfactants, making use of the application of phenomenological (semi-mechanistic), neural and fuzzy approaches. The phenomenological kinetic model of Ping Pong Bi Bi was fitted to experimental data. For this, kinetic data of the production of biosurfactants by esterification of oleic and lauric acids with fructose and lactose, using immobilized lipase B from Candida antarctica (CALB-IM-T2-350) and lipase from Pseudomonas fluorescens (PFL) immobilized on octyl-silica (silanized with octyltriethoxysilane), provided by LabEnz-UFSCar, were used. The classic Levenberg-Marquardt parameter fitting method was applied, resulting in a good correspondence between the proposed model and the experimental data. For validation of the semi-mechanistic model, a new set of experimental data was used, showing excellent predictive capacity of the model. Then, neural kinetic models were built using experimental data of enzymatic esterification of xylose with oleic and/or lauric acids, performed using the biocatalyst CALB-IM-T2-350 and CALB derivatives immobilized on Silica Magnetic Microparticles (SMMPs) with octyl groups (CALB-SMMP-octyl) or with octyl groups plus glutaraldehyde (CALB-SMMP-octyl-glu). Using Matlab Neural Network Toolbox, five artificial neural networks (ANNs) were trained, one for each type of biocatalyst and acid, obtaining R-squared values greater than 0.97. As a last effort in neural modeling, two ANNs were fitted (for two of the biocatalysts), each one of them already incorporating, in its inputs, an option referring to the type of acid. The R-squared values, above 0.98, also showed good predictability. Finally, modeling by fuzzy inference systems was studied, using the Neuro Fuzzy Designer tool from ANFIS (Adaptive Network-Based Fuzzy Inference System) of Matlab. Fuzzy models were built for each of the three biocatalysts under study (CALB-IM-T2-350, CALB-SMMP-octyl and CALB-SMMP-octyl-glu), considering as input linguistic variables the type of acid, temperature, reaction time and substrate molar ratio to predict the conversion of the esterification process. Gaussian membership functions and linear output functions were used, in a Takagi- Sugeno’s fuzzy approach. The parameters were fitted by a hybrid parametric optimization method. The results showed that the fuzzy model outputs were very close to the targets, with RMSE values below 0.006. To demonstrate the potential of fuzzy modeling to optimize processes, response surfaces were built for the conversion of xylose as a function of different operating conditions. The fuzzy surfaces indicated that higher values of conversion are reached after 45h of reaction, at temperatures above 50°C and at RMS of 1:0.2 (acid:sugar). Thus, the present work explored, in a broad way, all the capability of mathematical modeling, under different approaches, in the study of enzymatic production of biosurfactants.Reações enzimáticas de esterificação de ácidos graxos com carboidratos geram biossurfactantes, produtos com alta capacidade em reduzir tensões superficiais e interfaciais, aplicáveis principalmente nas indústrias alimentícia, farmacêutica e cosmética. A modelagem matemática, por sua vez, pode ser uma ferramenta útil, em suas diferentes abordagens, para simulação e otimização dos processos enzimáticos. Assim, este trabalho foi realizado em três etapas distintas e teve como objetivo a modelagem matemática de processos enzimáticos para a produção de biossurfactantes, fazendo uso da aplicação de abordagens fenomenológicas (semi-mecanísticas), neurais e nebulosas (fuzzy). O modelo cinético fenomenológico de Ping Pong Bi Bi foi ajustado a dados experimentais, fazendo-se uso de dados cinéticos da produção de biossurfactantes por esterificações de ácidos oleico e láurico com frutose e lactose, utilizando lipase B de Candida antarctica imobilizada (CALB-IM-T2-350) e lipase de Pseudomonas fluorescens (PFL) imobilizada em octil-sílica (sílica silanizada com octiltrietoxisilano), fornecidos pelo LabEnz-UFSCar. Foi aplicado o método clássico de ajuste de parâmetros de Levenberg-Marquardt resultando em uma boa correspondência entre o modelo proposto e os dados experimentais. Para validação do modelo, um novo conjunto de dados experimentais foi utilizado, mostrando excelente capacidade de previsão. Em seguida, foram construídos modelos cinéticos neurais utilizando dados experimentais de esterificações de xilose com ácidos oleico e/ou láurico, realizadas com emprego do biocatalisador CALB-IM-T2-350 e de derivados da CALB imobilizada em Micropartículas Magnéticas de Sílica (SMMPs) com grupos octil (CALB-SMMP-octil) ou com grupos octil mais glutaraldeído (CALB-SMMP-octil-glu). Fazendo uso do pacote Neural Network Toolbox do Matlab, foram treinadas cinco redes neurais artificiais (RNAs), uma para cada tipo de biocatalisador e ácido, obtendo-se valores de R-quadrático superiores a 0,97. Como um último esforço em modelagem neural, duas novas RNAs foram ajustadas com o agrupamento dos dados em relação ao tipo de biocatalizador. Os valores de R-quadrático, acima de 0,98, indicaram boa capacidade de previsão. Por fim, foi estudada a modelagem por sistema de inferência fuzzy, com uso da ferramenta Neuro Fuzzy Designer do ANFIS (Adaptive Network-Based Fuzzy Inference System) do Matlab. Foram construídos modelos fuzzy para cada um de três biocatalisadores em estudo (CALB-IM-T2-350, CALB-SMMP-octil e CALB-SMMP-octil-glu), considerando como variáveis linguísticas de entrada o tipo de ácido, temperatura, tempo de reação e a razão molar de substratos (RMS), para prever a conversão do processo de esterificação. Foram utilizadas funções de pertinência gaussianas e função linear para a saída, numa abordagem fuzzy de Takagi e Sugeno. Os parâmetros foram ajustados por um método de otimização paramétrica híbrido. Os resultados mostraram que as saídas dos modelos estavam muito próximas dos alvos, com valores de RMSE abaixo de 0,006. Visando demonstrar o potencial da modelagem nebulosa na otimização dos processos, foram construídas superfícies de resposta para a conversão da xilose em função de distintas condições operacionais. As superfícies nebulosas indicaram que maiores valores de conversão de xilose são atingidos a partir das 45h de reação, em temperaturas acima dos 50°C e na RMS de 1:0,2 (ácido:açúcar). Assim, o presente trabalho explorou a capacidade da modelagem matemática, sob distintas abordagens, no estudo da produção enzimática de biossurfactantes.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)145244/2017-2porUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Engenharia Química - PPGEQUFSCarAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessBiossurfactantesModelagem matemáticaPing Pong Bi BiRedes neurais artificiaisLógica fuzzyMathematical modelingArtificial neural networksFuzzy logicENGENHARIAS::ENGENHARIA QUIMICAModelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadasMathematical modeling of enzymatic syntheses of biosurfactants catalyzed by immobilized lipasesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis600600ab69fa78-14aa-4e78-beb8-e23c9aefadecreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALTESE - ALICE TORRES - FINAL_Revisada.pdfTESE - ALICE TORRES - FINAL_Revisada.pdfTese - versão finalapplication/pdf3578533https://repositorio.ufscar.br/bitstream/ufscar/14844/1/TESE%20-%20ALICE%20TORRES%20-%20FINAL_Revisada.pdf6e2ffba95c06f757398459313201b055MD51Declaração_Orientador Assinada.pdfDeclaração_Orientador Assinada.pdfCarta Comprovanteapplication/pdf181173https://repositorio.ufscar.br/bitstream/ufscar/14844/2/Declara%c3%a7%c3%a3o_Orientador%20Assinada.pdf26a6d0da1ac184b138552e72681e5301MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufscar.br/bitstream/ufscar/14844/3/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD53TEXTTESE - ALICE TORRES - FINAL_Revisada.pdf.txtTESE - ALICE TORRES - FINAL_Revisada.pdf.txtExtracted texttext/plain158094https://repositorio.ufscar.br/bitstream/ufscar/14844/4/TESE%20-%20ALICE%20TORRES%20-%20FINAL_Revisada.pdf.txt7bf4a30b956b25b107dd67affd6de535MD54Declaração_Orientador Assinada.pdf.txtDeclaração_Orientador Assinada.pdf.txtExtracted texttext/plain1280https://repositorio.ufscar.br/bitstream/ufscar/14844/6/Declara%c3%a7%c3%a3o_Orientador%20Assinada.pdf.txtb06ec781a110624d10bc596b4c44d96aMD56THUMBNAILTESE - ALICE TORRES - FINAL_Revisada.pdf.jpgTESE - ALICE TORRES - FINAL_Revisada.pdf.jpgIM Thumbnailimage/jpeg7347https://repositorio.ufscar.br/bitstream/ufscar/14844/5/TESE%20-%20ALICE%20TORRES%20-%20FINAL_Revisada.pdf.jpg2ae684e37b156c235230c138d85bf5d3MD55Declaração_Orientador Assinada.pdf.jpgDeclaração_Orientador Assinada.pdf.jpgIM Thumbnailimage/jpeg6119https://repositorio.ufscar.br/bitstream/ufscar/14844/7/Declara%c3%a7%c3%a3o_Orientador%20Assinada.pdf.jpg2c80ca70c76ddbbd80fdd59c0b60f19bMD57ufscar/148442023-09-18 18:32:15.009oai:repositorio.ufscar.br:ufscar/14844Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:32:15Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.por.fl_str_mv |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
dc.title.alternative.eng.fl_str_mv |
Mathematical modeling of enzymatic syntheses of biosurfactants catalyzed by immobilized lipases |
title |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
spellingShingle |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas Torres, Alice de Carvalho Lima Biossurfactantes Modelagem matemática Ping Pong Bi Bi Redes neurais artificiais Lógica fuzzy Mathematical modeling Artificial neural networks Fuzzy logic ENGENHARIAS::ENGENHARIA QUIMICA |
title_short |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
title_full |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
title_fullStr |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
title_full_unstemmed |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
title_sort |
Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas |
author |
Torres, Alice de Carvalho Lima |
author_facet |
Torres, Alice de Carvalho Lima |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/3634638433614642 |
dc.contributor.author.fl_str_mv |
Torres, Alice de Carvalho Lima |
dc.contributor.advisor1.fl_str_mv |
Sousa Júnior, Ruy de |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/1983482879541203 |
dc.contributor.authorID.fl_str_mv |
a74bd895-72a4-4664-a586-3e8961390770 |
contributor_str_mv |
Sousa Júnior, Ruy de |
dc.subject.por.fl_str_mv |
Biossurfactantes Modelagem matemática Ping Pong Bi Bi Redes neurais artificiais Lógica fuzzy |
topic |
Biossurfactantes Modelagem matemática Ping Pong Bi Bi Redes neurais artificiais Lógica fuzzy Mathematical modeling Artificial neural networks Fuzzy logic ENGENHARIAS::ENGENHARIA QUIMICA |
dc.subject.eng.fl_str_mv |
Mathematical modeling Artificial neural networks Fuzzy logic |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS::ENGENHARIA QUIMICA |
description |
Enzymatic reactions of esterification of fatty acids with carbohydrates generate biosurfactants, products with high capacity to reduce surface and interfacial tensions, applicable mainly in the food, pharmaceutical and cosmetic industries. Mathematical modeling, in turn, can be a useful tool, in its different approaches, for the simulation and optimization of enzymatic processes. Thus, this work was carried out in three distinct steps and aimed at the mathematical modeling of enzymatic processes to produce biosurfactants, making use of the application of phenomenological (semi-mechanistic), neural and fuzzy approaches. The phenomenological kinetic model of Ping Pong Bi Bi was fitted to experimental data. For this, kinetic data of the production of biosurfactants by esterification of oleic and lauric acids with fructose and lactose, using immobilized lipase B from Candida antarctica (CALB-IM-T2-350) and lipase from Pseudomonas fluorescens (PFL) immobilized on octyl-silica (silanized with octyltriethoxysilane), provided by LabEnz-UFSCar, were used. The classic Levenberg-Marquardt parameter fitting method was applied, resulting in a good correspondence between the proposed model and the experimental data. For validation of the semi-mechanistic model, a new set of experimental data was used, showing excellent predictive capacity of the model. Then, neural kinetic models were built using experimental data of enzymatic esterification of xylose with oleic and/or lauric acids, performed using the biocatalyst CALB-IM-T2-350 and CALB derivatives immobilized on Silica Magnetic Microparticles (SMMPs) with octyl groups (CALB-SMMP-octyl) or with octyl groups plus glutaraldehyde (CALB-SMMP-octyl-glu). Using Matlab Neural Network Toolbox, five artificial neural networks (ANNs) were trained, one for each type of biocatalyst and acid, obtaining R-squared values greater than 0.97. As a last effort in neural modeling, two ANNs were fitted (for two of the biocatalysts), each one of them already incorporating, in its inputs, an option referring to the type of acid. The R-squared values, above 0.98, also showed good predictability. Finally, modeling by fuzzy inference systems was studied, using the Neuro Fuzzy Designer tool from ANFIS (Adaptive Network-Based Fuzzy Inference System) of Matlab. Fuzzy models were built for each of the three biocatalysts under study (CALB-IM-T2-350, CALB-SMMP-octyl and CALB-SMMP-octyl-glu), considering as input linguistic variables the type of acid, temperature, reaction time and substrate molar ratio to predict the conversion of the esterification process. Gaussian membership functions and linear output functions were used, in a Takagi- Sugeno’s fuzzy approach. The parameters were fitted by a hybrid parametric optimization method. The results showed that the fuzzy model outputs were very close to the targets, with RMSE values below 0.006. To demonstrate the potential of fuzzy modeling to optimize processes, response surfaces were built for the conversion of xylose as a function of different operating conditions. The fuzzy surfaces indicated that higher values of conversion are reached after 45h of reaction, at temperatures above 50°C and at RMS of 1:0.2 (acid:sugar). Thus, the present work explored, in a broad way, all the capability of mathematical modeling, under different approaches, in the study of enzymatic production of biosurfactants. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-09-10T12:40:53Z |
dc.date.available.fl_str_mv |
2021-09-10T12:40:53Z |
dc.date.issued.fl_str_mv |
2021-07-08 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
TORRES, Alice de Carvalho Lima. Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas. 2021. Tese (Doutorado em Engenharia Química) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14844. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/14844 |
identifier_str_mv |
TORRES, Alice de Carvalho Lima. Modelagem matemática para sínteses enzimáticas de biossurfactantes catalisadas por lipases imobilizadas. 2021. Tese (Doutorado em Engenharia Química) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14844. |
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https://repositorio.ufscar.br/handle/ufscar/14844 |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
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openAccess |
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Universidade Federal de São Carlos Câmpus São Carlos |
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Programa de Pós-Graduação em Engenharia Química - PPGEQ |
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UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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