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Previous issue date: 2013-12-18 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Expanded Bed Adsorption (EBA) is an integrative process that combines concepts of chromatography and fluidization of solids. The many parameters involved and their synergistic effects complicate the optimization of the process. Fortunately, some mathematical tools have been developed in order to guide the investigation of the EBA system. In this work the application of experimental design, phenomenological modeling and artificial neural networks (ANN) in understanding chitosanases adsorption on ion exchange resin Streamline? DEAE have been investigated. The strain Paenibacillus ehimensis NRRL B-23118 was used for chitosanase production. EBA experiments were carried out using a column of 2.6 cm inner diameter with 30.0 cm in height that was coupled to a peristaltic pump. At the bottom of the column there was a distributor of glass beads having a height of 3.0 cm. Assays for residence time distribution (RTD) revelead a high degree of mixing, however, the Richardson-Zaki coefficients showed that the column was on the threshold of stability. Isotherm models fitted the adsorption equilibrium data in the presence of lyotropic salts. The results of experiment design indicated that the ionic strength and superficial velocity are important to the recovery and purity of chitosanases. The molecular mass of the two chitosanases were approximately 23 kDa and 52 kDa as estimated by SDS-PAGE. The phenomenological modeling was aimed to describe the operations in batch and column chromatography. The simulations were performed in Microsoft Visual Studio. The kinetic rate constant model set to kinetic curves efficiently under conditions of initial enzyme activity 0.232, 0.142 e 0.079 UA/mL. The simulated breakthrough curves showed some differences with experimental data, especially regarding the slope. Sensitivity tests of the model on the surface velocity, axial dispersion and initial concentration showed agreement with the literature. The neural network was constructed in MATLAB and Neural Network Toolbox. The cross-validation was used to improve the ability of generalization. The parameters of ANN were improved to obtain the settings 6-6 (enzyme activity) and 9-6 (total protein), as well as tansig transfer function and Levenberg-Marquardt training algorithm. The neural
Carlos Eduardo de Ara?jo Padilha dezembro/2013
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networks simulations, including all the steps of cycle, showed good agreement with experimental data, with a correlation coefficient of approximately 0.974. The effects of input variables on profiles of the stages of loading, washing and elution were consistent with the literature / A adsor??o em leito expandido (ALE) ? uma t?cnica integrativa que alia conceitos de cromatografia e fluidiza??o de s?lidos. A diversidade de par?metros envolvidos e seus efeitos sinerg?ticos dificultam a tarefa de otimiza??o da opera??o. Felizmente, algumas ferramentas matem?ticas foram desenvolvidas de modo a direcionar as investiga??es do sistema ALE. Assim, o presente trabalho prop?e a aplica??o do planejamento experimental, modelagem fenomenol?gica e redes neurais artificiais (RNAs) na compreens?o da adsor??o de quitosanases na resina de troca i?nica Streamline? DEAE. A cepa Paenibacillus ehimensis NRRL B-23118 foi respons?vel pela produ??o das quitosanases. Nos ensaios de adsor??o usando o leito na forma expandida foi utilizada uma coluna de 2,6 cm de di?metro por 30,0 cm de altura, acoplada a uma bomba perist?ltica. Na base da coluna existia um distribuidor de microesferas de vidro com altura de 3,0 cm. Os ensaios de determina??o de tempo de resid?ncia (DTR) revelaram elevado grau de mistura, entretanto, os coeficientes de Richardson-Zaki mostraram que a coluna estava no limiar da estabilidade. Pelas regress?es das isotermas puderam-se ajustar os dados de equil?brio de adsor??o, na presen?a de diferentes sais da escala liotr?pica. O resultado do planejamento apontou que a for?a i?nica e a velocidade influenciam a recupera??o e pureza das quitosanases. As massas moleculares das duas esp?cies de quitosanases foram estimadas por SDS-PAGE, obtendo-se aproximadamente 23 kDa e 52 kDa. A modelagem fenomenol?gica foi direcionada para descrever as opera??es em batelada e na coluna cromatogr?fica. As simula??es foram executadas no Microsoft Visual Studio, usando a linguagem Fortran. O modelo de taxa constante ajustou-se ?s curvas cin?ticas com excel?ncia, nas condi??es de atividade iniciais 0,232, 0,142 e 0,079 UA/mL. As curvas de ruptura simuladas apresentaram algumas disparidades com os dados experimentais, principalmente quanto ? inclina??o. Os testes de sensibilidade do modelo sobre a velocidade superficial, dispers?o axial e concentra??o inicial mostraram conformidade com artigos publicados. A rede neural foi constru?da no ambiente MATLAB, por meio da Neural Network Toolbox. A valida??o cruzada foi usada para melhorar a capacidade de generaliza??o.
Carlos Eduardo de Ara?jo Padilha dezembro/2013
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Aperfei?oaram-se os par?metros da RNA at? se obter as configura??es 6-6 (atividade enzim?tica) e 9-6 (prote?nas totais), fun??o de ativa??o tansig e algoritmo de treinamento Levenberg-Marquardt. As simula??es da rede neural, incluindo todo o ciclo da opera??o, mostraram boa concord?ncia com os dados experimentais, com coeficiente de correla??o da ordem de 0,974. Os efeitos das vari?veis de entrada sobre os perfis das etapas de carga, lavagem e elui??o foram compat?veis com a literatura
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/15849 |
| Date | 18 December 2013 |
| Creators | Padilha, Carlos Eduardo de Ara?jo |
| Contributors | CPF:87510383404, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4799564Y2, Porto, Ana L?cia Figueiredo, CPF:25514776468, http://lattes.cnpq.br/4989617783837981, Souza, Domingos Fabiano de Santana, CPF:90595920500, http://lattes.cnpq.br/7400460833577257, Oliveira, Jackson Ara?jo de, Santos, Everaldo Silvino dos |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Engenharia Qu?mica, UFRN, BR, Pesquisa e Desenvolvimento de Tecnologias Regionais |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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