Development of Polymer Resins using Neural Networks

Abstract

The development of polymer resins can benefit from the application of neural networks, using its great ability to correlate inputs and outputs. In this work we have developed a procedure that uses neural networks to correlate the end-user properties of a polymer with the polymerization reactor's operational condition that will produce that desired polymer. This procedure is aimed at speeding up the development of new resins and help finding the appropriate operational conditions to produce a given polymer resin; reducing experimentation, pilot plant tests and therefore time and money spent on development. The procedure shown in this paper can predict the reactor's operational condition with an error lower than 5%

Artificial Neural Networks Associated to Calorimetric Measurements Used as a Method to Predict Polymer Composition of High Solid Content Emulsion Copolymerizations

“…Starting from kinetic properties of the monomers, overall conversion, mechanistic models of emulsion polymerization, and using advanced mathematical tools, it is able to infer the evolution of the polymeric composition. [15] Such methodology will be tested on this work for high solid contents latexes.…”

“…The results are compared to those obtained by the high gain state observer developed by Othman and Févotte. [10,15] The ANN used was a feed-forward back-propagation, with one hidden layer connected through sigmoidal transfer function, the number of neurons in the hidden layer varied according to the studied case, from four to six. The training process was carried out using the Levenberg-Marquardt algorithm, since it has been successfully used in related papers.…”