Hybrid Neural Network Models Applied to a Free Radical Polymerization Process

Abstract

This article presents different ways of obtaining hybrid models, which are composed of a simplified phenomenological model and one or several neural networks. As an example, we consider free radical polymerization of methyl methacrylate, achieved through a batch bulk process, in which modeling of conversion and polymerization degrees is analyzed. Kinetics of the process is described through a simplified phenomenological model that does not take into account the gel and glass effects. This last part of the process, which is more difficult to model, is rendered by means of feed-forward neural networks with one or two hidden layers. In the present paper, the hybridization procedure is made in three ways: 1) the neural network corrects the outputs of the simplified kinetic model by modeling the residuals of conversion and polymerization degrees; 2) the neural network provides accurate values of the rate constants to the simplified kinetic model; 3) the neural network models that part of the process in which gel and glass effects appear. It is demonstrated that accurate results are obtained in all three cases, and the hybrid models are easily created and manipulated, especially because they are based on neural networks with quite simple topologies.