Abdul-Fattah Aly Asfour

The present paper reports experimental densities and kinematic viscosities of ten binary mixtures of 1-hexanol, ethylbenzene, octane, p-xylene, and chlorobenzene at 293.15 K and 298.15 K. The experimental data were obtained using Cannon-Ubbelohde viscometers and Anton-Paar DMA 60 density meter under temperature control. The experimental data were correlated with five different models: Allan-Teja, GC-UNIMOD, the Generalized Corresponding States Principle (GCSP), generalized McAllister three-body interaction, and an Artificial Neural Network (ANN) viscosity model. Among the tested models, the generalized McAllister three-body interaction model yielded the most accurate predictions, with an overall average absolute deviation of 3.9%. The Allan-Teja correlation exhibited the largest deviations for binary mixtures containing chlorobenzene. Two specialized artificial neural networks (ANN) models were developed to predict kinematic viscosities at 293.15 K and 298.15 K: one trained on 1-hexanol-containing mixtures and the other on non-associating hydrocarbon mixtures. Each model employed a 6–4–1 feedforward architecture with backpropagation training. The class-specific ANN models achieved average %AAD values of 3.3% and 1.6%, respectively. However, these models are limited to the mixture classes on which they were trained and cannot be generalized to other systems.