PREDICTION OF BOILING POINTS AND CRITICAL-TEMPERATURES OF INDUSTRIALLY IMPORTANT ORGANIC-COMPOUNDS FROM MOLECULAR-STRUCTURE

Numeric representations of molecular structure are used to predict the normal boiling points and critical temperatures for compounds drawn from the Design Institute for Physical Property Data (DIPPR) database. Multiple linear regression analysis and computational neural networks (i.e., using back-propagation and quasi-Newton training) are, employed to develop models which can accurately predict the boiling points of 298 organic compounds. This approach is assessed by comparing its results against results obtained using the Joback group contribution approach. Finally, the same methodology is used to develop two separate critical temperature models, one based on the methods of corresponding states and the second based on structurally derived parameters alone.