Application of Artificial Neural Network to Estimate Solubility of New Alternative Compounds as Cleaning and Blowing agents

Abstract

Artificial neural network (ANN) technique was applied to estimate solubility of several alternative compounds for CFCs and HCFCs. In this estimation method, we used both information on interaction parameters derived from molecular simulation code 'Cerius2/Blends (MSI)' and some physical properties such as liquid density, molecular volume and molecular weight. At first, this presented method succeeded in estimating the solubility of fluorinated compounds with n-Eicosane (C20H42). Furthermore, the solubility of fluorinated compounds with polyol PE-315 (aromatic polyol, OH value = 315mgKOH/g) was estimated accurately by adding maximum charge of hydrogen in fluorinated compounds as input parameter. Finally, constructed ANN was applied to screen new alternative compounds and HFE-236pc (CHF2CF2OCHF2) was selected as new candidates for blowing agents.