Optimization of Batch Polymerization Reactors Using Neural-Network Rate-Function Models

A simulated verification and validation of the neural-network rate-function (NNRF) approach to modeling the nonlinear dynamic systems is provided. The NNRF modeling scheme utilizes some a priori process knowledge and experimental data to develop a dynamic neural-network model. Based on the obtained neural-network model, an optimal temperature trajectory was computed via the two-step method to drive a batch free-radical polymerization reaction to a prescribed molecular weight distribution (MWD). Evaluation of the quality of the end product suggests that the proposed NNRF modeling approach can be applied in dynamic modeling of a complex and nonlinear reaction system.