NEURAL NETWORK MODELLING OF THE EQUILIBRIUM ANIONIC POLYMERIZATION OF CYCLIC SILOXANES The kinetics of the equilibrium anionic polymerization of some cyclic siloxanes is modelled by using neural networks. Feedforward neural networks with one or two hidden layers have been used to appreciate the rates of disappearance of octamethylcyclotetrasiloxane and aminopropyl disiloxane at different catalyst concentrations (direct modelling). Alternatively, another neural model has been developed to estimate the amount of catalyst, which leads to an imposed final concentration of siloxane (inverse modelling). Experimental data for the polymerization of octamethylcyclotetrasiloxane in the presence of KOH as a catalyst and 1,3-bis(aminopropyl)tetramethyldisiloxane as a functional endblocker were used as training data sets for neural models. Satisfactory agreement between experimental data and network predictions obtained in validation phases proved that the projected models have good generalization capacities and, consequently, they describe well the process.
Keywords: neural networks, direct and inverse neural modelling, polysiloxane, cyclic siloxanes, anionic ring opening polymerization