Monitoramento e avaliação de desempenho de sistemas MPC utilizando métodos estatísticos multivariados

• Ano de defesa: 2017
• Autor(a) principal: Fontes, Nayanne Maria Garcia Rego
• Orientador(a): Sotomayor, Oscar Alberto Zanabria
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Sergipe
• Programa de Pós-Graduação: Pós-Graduação em Engenharia Elétrica
• País: Brasil
• Palavras-chave em Português: Engenharia elétrica; Controle de processo; Controlador preditivo; Análise de Componentes Principais (ACP); Análise de componentes independentes; Monitoramento; Model Predictive Control (MPC); Principal Component Analysis (PCA); Independent Component Analysis (ICA)
• Palavras-chave em Inglês: Monitoring
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://ri.ufs.br/handle/riufs/5037

Monitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control.