This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM) is based on fundamental conservation laws associated with a neural network (NN) used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN) used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.

or multivariable non linear predictive control implementations, a hybrid-neural model (lumped model) was successfully used for modeling a loop-tubular polymerization reactor (a lumped or distributed model, depending on recycle ratio). Bifurcation diagrams were computed in order to investigate the agreement between process and model, of paramount importance for model based controller implementation purposes. Performance was evaluated considering the nonlinear model predictive control of both a loop tubular reactor (lumped) SISO problem and a tubular reactor (distributed) MIMO problem.