PREDICTION OF COPOLYMER COMPOSITION DRIFT USING ARTIFICIAL NEURAL NETWORKS - COPOLYMERIZATION OF ACRYLAMIDE WITH QUATERNARY AMMONIUM CATIONIC MONOMERS

The free radical copolymerization of acrylamide with a quaternary ammo nium cationic comonomer, diethylaminoethyl acrylate (DMAEA), has been investigated in inverse-emulsion. The copolymer composition was determ ined from residual monomer concentrations using an h.p.l.c. method. Bo th reactivity ratios were observed to change with conversion. Furtherm ore, the reactivity ratio of the cationic monomer was found to be a fu nction of the ionic strength and monomer concentration and, to a limit ed extent, the polymer concentration and the organic-to-aqueous phase ratio. Therefore, the classical binary ultimate group copolymerization scheme cannot predict copolymer composition drift throughout the reac tion. An artificial neural network (ANN) has been built to predict the copolymer composition. ANNs have the ability to map nonlinear relatio nships without a priori process information. The results show that an ANN can predict the copolymer composition very well as a function of r eaction conditions and conversion. It is expected that for any system where the reactivity ratios are conversion dependent that an ANN, such as the one developed herein, will be preferable. (C) 1997 Elsevier Sc ience Ltd.