Work

Densities and viscosities of binary liquid mixtures of chlorobenzene, p-xylene, octane,1-hexanol and ethylbenzene at (293.15 and 298.15) K

Year: 2026

Type: article

Abstract: 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, with combined measurement uncertainties evaluated for density, kinematic viscosity, and mole-fraction. 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 3.71%. The Allan-Teja correlation exhibited the largest deviations for binary mixtures containing chlorobenzene and 1-hexanol, a result attributable to its carbon-number-only parameterization, which contains no term for polarity or hydrogen bonding. 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 full experimental dataset comprises 220 composition points, of which the non-associating class contributes 132 points and the 1-hexanol class 88 points: each subset was split 50/50 between training and independent testing. The class-specific ANN models achieved average %AAD values of 3.35% and 1.55%, whereas a single generalized ANN trained on the combined dataset gave 25.80% overall AAD. These class-specific values demonstrate interpolation within the mixture classes on which the networks were trained; they are not evidence of prediction for binary systems outside those classes.

Source: Chemical Thermodynamics and Thermal Analysis

DOI: 10.1016/j.ctta.2026.100376

Authors: Nidal M. Hussein, Abdul-Fattah A. Asfour, Haneen H. Darwish, Ahmad J. Alzubaidi, Ahmed Y. Tawfik

Institutions: Petra University, University of South Alabama, University of Windsor, Microsoft (United States)

Language: English

Topic: Thermodynamic properties of mixtures

Subfield: Fluid Flow and Transfer Processes

Field: Chemical Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,350

Funder: Natural Sciences and Engineering Research Council of Canada