Modeling composite emulsion polymerization kinetics

Abstract

The conversion profiles of a number of factorial designed experiments used to study composite emulsion polymerization were modeled using a deterministic mathematical construct as well as an empirical neural network approach. In the deterministic modeling approach, existing mechanistic models for emulsion polymerization were employed for which estimates of rate constants were obtained from established literature sources as well as experiments. Fitting of the kinetic data was done using nonlinear fitting algorithms to adjust the estimated rate constants to provide the best fit of the conversion profiles. In the case of the empirical modeling using neural networks, the neural net inputs were in the form of the factor levels of the various experimental designs. Several nonrelated experimental designs could be combined in this way to serve as the input, whereas the conversion profiles were targeted as outputs. Following the successful implementation of both modeling strategies, a hybrid modeling approach was tested by combining the neural network predictive power to estimate values for rate constants while retaining the aforementioned mechanistic models to fit the data. C