Nonlinear predictive control of an industrial slurry reactor

Abstract

A nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements.

Técnica de controle preditivo baseado em modelo aplicada ao controle de tensão de um gerador síncrono - resultados experimentais

“…O sinal de controle otimizadoé sintetizado com base na minimização de um critério de desempenho e em um conjunto de restrições, adequadamente definidos para cada aplicação. O MPC está amplamente difundidos no meio acadêmico (Bravo and Normey-Rico, 2009) e em diversas aplicações na indústria de processos (Fontes and Mendes, 2009) e de geração de energia (Barra et al, 2005). Um importante membro da família de algoritmos MPC, o Generalized Predictive Controlller (GPC), foi proposto em 1987 por Clarke (Clarke et al, 1987a;Clarke et al, 1987b;Clarke and Zhang, 1987).…”

Novel Switch Stabilizing Model Predictive Control Strategy Applied in the Control of a Simulated Moving Bed for the Separation of Bi-Naphthol Enantiomers

“…It is known from simulations that the NMPC strategies present better performance than linear MPC formulations. − Nonetheless, the practical application of NMPC solutions is still not at an acceptable stage for implementation purposes. The main limitations for this are as follows: the absence of systematic methods to tune the controller parameters; very restrictive conditions to guarantee stability and feasibility of the closed-loop system; and convergence issues of NMPC coupled to the state estimator.…”