Artificial neural networks associated to calorimetry to preview polymer composition of high solid content emulsion copolymerizations

Artificial neural networks (ANN) have demonstrated to be powerful tools to model nonlinear systems, such as high solid content latexes produced by emulsion polymerisation. This system has a great importance in the polymeric industry, essentially for environmental reasons, since they usually have water as continuous phase. In order to propose technical and economically feasible alternatives to control polymeric structure, this work is aimed to develop a new methodology based on artificial neural networks associated with calorimetry to preview polymeric structure. The designed artificial neural networks presented excellent results when tested with process condition variations as well as when they were submitted to test concerning to the variation on the proportion of monomers in the latex formulation. Hence, it was possible to conclude that artificial neural networks, associated to calorimetry, lead to an efficient method to preview the polymer composition in emulsion copolymerizations.