Controle preditivo baseado em modelo com estimação de estados restrita para controle e monitoramento de processos não lineares - Carvalho, Romero Florentino de (2017)
The present master degree work discussed about monitoring and control problem with a stochastic approach based in particle filter with application in typical problems of Chemical Engineering, which mostly, are nonlinear and have uncertainty non necessarily Gaussian. The chemical process control is of paramount importance in order to guarantee the quality and safety needs, beyond economic and operational viability. However, many of the variables linked to these objectives are not online measurement or present significant uncertainties. For this, is necessary consider estimation tools for the enable online control. In this sense, the present dissertation investigated a stochastic approach for the model predictive control (MPC), in which the MPC scheme is combined with particle filter (PF), which is a Monte Carlo sequential estimation tools type for nonlinear systems and non-Gaussian uncertainties. This proposal also considers optimizations methods to the particles projection that violates the physical or operational constraints, in a viable region, originating the scheme with initials CPF-MPC. This proposal extends results from previous work by including constraints in the process output variables. This scheme was applied to two case studies: non-isothermal CSTR reactor with simple first order reaction and CSTR with van der Vusse kinetics. Finally, the PF-MPC scheme was applied in a run-of-mine ore milling circuit and compared to a PI control. The CPFMPC results show a good and satisfactory performance to meet the control objectives, guaranteeing a relatively low computational time, reduction of the offset and process disturbance.