Desenvolvimento de sistema nebuloso (fuzzy) para controle do oxigênio dissolvido no cultivo de escherichia coli para expressão de proteínas recombinantes

• Autor(a) principal: Akisue, Rafael Akira
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/20.500.14289/9876
• topic: Oxigênio dissolvido; Redes neurais artificiais; Lógica fuzzy; Escherichia coli recombinante; SUPERSYS_HCDC; Dissolved Oxygen; Artificial neural networks; Recombinant escherichia coli; Fuzzy logic; ENGENHARIAS::ENGENHARIA QUIMICA::PROCESSOS INDUSTRIAIS DE ENGENHARIA QUIMICA

One very important bioprocess is the cultivation of recombinant E. coli for expression of heterologous proteins. For this, High Cell Density Culture is one of the most widely used technique. Although it results in cell densities above 100 g/L, it also has its challenges. Therefore, researchers from the Laboratory of Development and Automation of Bioprocesses (LaDABio) at Chemical Engineering Department of Federal University of São Carlos (UFSCar) developed a robust computer program (SUPERSYS_HCDC) that, among other functions, presents a hybrid system with a Proportional-Integral-Derivative (PID) controller for agitation speed control and a decision tree to manipulate air and oxygen flow rates that control the percentage of dissolved oxygen in the cultivation (nowadays some commercial controllers also offer this cascade control). However, in particular, delays may occur in the devices responsible for air and oxygen injection in the bioreactor, since the decision tree provides no smooth responses (that is, no gradual transitions in the control action). The system presented operates by introducing steps in the air and oxygen flow rates. Under the light of the above-mentioned facts, fuzzy reasoning was used to develop a fuzzy controller, aiming to improve dissolved oxygen control in recombinant E. coli cultivation for heterologous protein production. At first, fuzzy logic toolbox was used to generate a control algorithm implemented in a MATLAB code. Secondly, the membership functions parameters were optimized using ANFIS tool. Finally, in order to perform tests using the fuzzy controller, it was coupled to a neural network model of the process. This was created using artificial neural network toolbox and E. coli cultivation data. Results for oxygen and air flow rates indicated that the trends of aeration required by E. coli cultivation were fulfilled. Using fuzzy controller, it was possible to maintain the percentage of dissolved oxygen around the setpoint value of 30%. In general, the fuzzy controller responses were smoother than those provided by the decision tree, in a way that the dissolved oxygen peaks were softened.