Modelagem, simulação e ajuste de condições operacionais da etapa de crescimento celular do bioprocesso industrial de produção de farneseno

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Souza, Fabiano Antonio de
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Estadual Paulista (Unesp)
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11449/193338
Resumo: SOUZA, F. A. Modeling, simulation and adjustment of operational conditions of the cell growth stage of the industrial bioprocess of farnesene production (Master in Biomaterials and Bioprocess Engineering) - School of Pharmaceutical Sciences, Araraquara, São Paulo State University (UNESP), Araraquara, 2020. Currently, an increasing number of industries have used bioprocesses to convert sugars into different products. Based on the demand for bioproducts, it is essential to achieve high production volumes and, in this context, any losses or delays have a negative impact on the productivity of the bioprocess. Since the identification and control of scale-dependent fermentation parameters are essential to achieve a high-performance operation at an industrial level, the optimization of the intervening variables in the fermentation process becomes a necessary task that can be facilitated by the use of process modeling and simulation techniques. In this sense, the objective of this study was to model, simulate and adjust the operational conditions of the second phase of the cell multiplication stage of the industrial bioprocess for producing farnesene. Based on a non-segregated and unstructured approach to microbial cells, the proposed mathematical model consisted of two ordinary differential equations representative of cell mass and substrate balances in the IF (Initial Fermenter). For the numerical integration of the differential equations, the 4th-order Runge-Kutta-Gill method was used and for the adjustment of the model's kinetic parameters, the least squares method was used together with the Marquardt algorithm. The results obtained showed that the proposed mathematical model satisfactorily described the behavioral trend of the modeled state variables (substrate concentration and optical density) for two sets of batches performed at pressures of 1.0 and 0.5 bar applied in the IF, validating the model for studies of simulation, optimization and control of the bioprocess. From simulations taking into account various aspects of the bioprocess such as inoculum size, supplementation of the culture medium with growth enhancing substrates, the possibility of occurrence of inhibition phenomena and cultivation mode (batch or batch fed), it was possible to adjust the operational conditions of the cell growth stage in the IF aiming to reach the target values of optical density and cell growth time specified for this stage of the industrial bioprocess of farnesene production.