Prediction of lower critical solution temperature of N-isopropylacrylamide-acrylic acid copolymer by an artificial neural network model.

In this paper, we have investigated the lower critical solution temperature (LCST) of N-isopropylacrylamide-acrylic acid (NIPAAm-AAc) copolymer as a function of chain-transfer agent/initiator mole ratio, acrylic acid content of copolymer, concentration, pH and ionic strength of aqueous copolymer solution. Aqueous solutions with the desired properties were prepared from previously purified polymers, synthesized at 65 degrees C by solution polymerization using ethanol. The effects of each parameter on the LCST were examined experimentally. In addition, an artificial neural network model that is able to predict the lower critical solution temperature was developed. The predictions from this model compare well against both training and test data sets with an average error less than 2.53%.