Data-Driven Model Based Control of a Multi-Product Semi-Batch Polymerization Reactor

Generic model control (GMC) has been successfully used for achieving tight control of batch/semi-batch processes. As the requirement to developing a mechanistic model can prove to be a bottle-neck while implementing GMC, many researchers have recently proposed GMC formulations based on black box models developed using artificial neural networks (ANN). The applicability of most of these formulations is limited to continuously operated systems with relative degree one. In addition, these formulations cannot handle constraints on inputs systematically. In the present study, ANN based GMC (ANNGMC) approach is extended to semi-batch processes with relative order higher than one. The nonlinear time-varying behaviour of batch/semi-batch processes is approximated using ANN model developed in the desired operating region. The ANN model is further used to formulate a nonlinear controller using GMC framework for solving trajectory-tracking problems associated with semi-batch reactors. The control problem at each sampling instant is formulated as a constrained optimization problem whereby the constraints on manipulated inputs can be handled systematically. The proposed controller formulation is used for solving trajectory-tracking problems associated with semi-batch reactors. The performance of the proposed control algorithm is evaluated by simulating the challenge problem proposed by Chylla and Haase (1993), which involves temperature control of a multi-product semi-batch polymerization reactor under widely varying operating conditions. The simulation exercise reveals that the performance of proposed ANNGMC formulation is comparable to the performance of the GMC formulation based on the exact mechanistic model, and is much better than PID controller performance.