Estimation of solid vapor pressures of pure compounds at different temperatures using a multilayer network with particle swarm algorithm

Solid vapor pressures (P^S) of pure compounds have been estimated at several temperatures using a hybrid model that includes an artificial neural network with particle swarm optimization and a group contribution method. A total of 700 data points of solid vapor pressure versus temperature, corresponding to 70 substances, have been used to train the neural network developed using Matlab. The following properties were considered as input parameters: 36 structural groups, molecular mass, dipole moment, temperature and pressure in the triple point (upper limit of the sublimation curve), and the limiting value P^S → 0 as T → 0 (lower limit of the sublimation curve). Then, the solid vapor pressures of 28 other solids (280 data points) have been predicted and results compared to experimental data from the literature. The study shows that the proposed method represents an excellent alternative for the prediction of solid vapor pressures from the knowledge of some other available properties and from the structure of the molecule.