Application of Neural Networks to Modeling and Estimating Temperature-Dependent Liquid Viscosity of Organic Compounds

Abstract

Back-propagation neural network models for correlating and predicting the viscosity - temperature behavior of a large variety of organic liquids were developed. Experimental values for the liquid viscosity for 1229 data points from 440 compounds containing C, H, N, O, S, and all halogens have been collected from the literature. The data ranges covered are from -120 to 160 °C for temperature and from 0.164 (trans-2- pentene at 20 °C) to 1.34 × 105 (glycerol at -20 °C) mPa·s for viscosity value. After dividing the total database of 440 compounds into training (237 with 673 data points), validation (124 with 423 data points), and test (79 with 133 data points) sets, the modeling performance of two separate neural network models with different architectures, one based on a compound-specific temperature dependence and the second based on a compound-independent one, has been examined. The resulting former model showed somewhat better modeling performance than latter, and the model gave squared correlation coefficients of 0.956, 0.932, and 0.884 and root mean-squares errors of 0.122, 0.134, and 0.148 log units for the training, validation, and test sets, respectively. The input descriptors include molar refraction, critical temperature, molar magnetic susceptibility, cohesive energy, temperatures, and five kinds of indicator variables for functionalities, alcohols/ phenols, nitriles, amines, amides, and aliphatic ring. The reliability of the proposed model was assessed by comparing the results against calculated viscosities by two existing group-contribution approaches, the method of van Velzen et al. and the Joback and Reid method.