Advanced control of propylene polimerizations in slurry reactors

Abstract

The objective of this work is to develop a strategy of nonlinear model predictive control for industrial slurry reactors of propylene polymerizations. The controlled variables are the melt index (polymer quality) and the amount of unreacted monomer (productivity). The model used in the controller presents a linear dynamics and a nonlinear static gain given by a neuronal network MLP (multilayer perceptron). The simulated performance of the controller was evaluated for a typical propylene polymerization process. It is shown that the performance of the proposed control strategy is much better than the one obtained with the use of linear predictive controllers for setpoint tracking control problems

Dynamic optimization of semibatch vinyl acetate/acrylic acid suspension copolymerizations

“…To solve the proposed optimization problem, a large number of distinct analytical and numerical techniques have been used [7][8][9][15][16][17][18][19][20]. For instance, methods based on the maximum principle of Pontryagin and on the optimal control theory have been used to provide closed analytical solutions for the optimum conditions; methods based on the use of Lagrange multipliers have been used to provide closed analytical and numerical solutions to constrained problems; methods based on orthogonal collocation procedures have been combined with successive quadratic programing to provide polynomial approximations of the optimal dynamic trajectories; direct search optimization techniques have been used to provide optimal solutions over a discretized time domain; among many other possibilities.…”