Controle preditivo não linear para sistemas de parâmetros distribuídos

• Ano de defesa: 2014
• Autor(a) principal: Rodríguez, Diana Esperanza Sandoval
• Orientador(a): Kwong, Wu Hong
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Química - PPGEQ
• País: BR
• Palavras-chave em Português: Engenharia química; Controle preditivo não-linear; Reator de branqueamento; Colocação ortogonal; Colocação equidistante; Diferenças finitas
• Palavras-chave em Inglês: Nonlinear predictive control; Reactor bleaching; Orthogonal collocation; Finite difference
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA QUIMICA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/4151

In general, the chemical processes can be represented using mathematical models, in the case of lumped systems include ordinary differential equations, or, partial differential equations, when distributed parameter systems using methods is necessary in both cases numerical resolution in these models, with the purpose to simulate, analyze and control the process. The implementation of control systems in chemical processes, brings with it many advantages, among these, the improvement and stability in production rates, ensuring product quality and the possibility of a safe operation of the process. Thus, in the last 30 years, different control methodologies were developed, one of the most used techniques, the Model Predictive Control Based on. Its success is due to the fact that this type of control accepts constraints on input variables and process output, determining the future of this movement, while optimizing an objective function can lead to the output of the process until the desired set point. The objective of this project is to implement the Model Predictive Controller with Nonlinear (CPNL ) for a reactor pulp bleaching by the use of chlorine dioxide, whose mathematical model is comprised of partial differential equations, thus being a model parameter distributed. Implementation of the controller, the plant is discretized by the Finite Difference Method and the process model is solved with the technique of Orthogonal Collocation. The integration of the resulting ordinary differential equations systems is performed by the method of Runge-Kutta. The Predictive Controller was compared with a Controller Proportional-Integral (PI). Studies have shown that CPNL has better performance, with faster response and values of the Integral Absolute Error (IAE) and Integral Square Error (ISE) smaller than those calculated for the PI controller.