Neural Network Modeling Applied to Polyacrylamide Based Hydrogels Synthetized By Single Step Process

Abstract

This paper presents a series of experimental data obtained from the synthesis of polyacrylamide-based hydrogels and a general neural network methodology that accomplishes the modeling and optimization of the polymerization process.

Using direct neural network modeling, the variation of the main parameters in the synthesis of polyacrylamide-based hydrogels (polymerization yield and maximum swelling degree) was modeled in correlation with reactant concentrations, temperature, and reaction time. The predictions of the network, verified against initial training data and other testing data in the domain of the reaction conditions, were quite precise.

Inverse neural modeling determines, in a facile manner and with good results, the initial reaction conditions, which lead to a preestablished reaction yield and maximum swelling degree. This optimization method is more advantageous compared to a difficult classical procedure that requires a good mathematical model and an optimization solving technique.

Keywords:

• direct and inverse neural network modeling
• hydrogels, modeling and optimization strategy
• swelling degree

ACKNOWLEDGMENT

This research occurred in the framework of the Projects PN I CEEX 40(510)/2005 and PN II PC 71-006/2007, for which the authors acknowledge financing assistance.