Modeling the Interfacial Tension of Water-Based Binary and Ternary Systems at High Pressures Using a Neuro-Evolutive Technique

Yasser Vasseghian; Alireza Bahadori; Alireza Khataee; Elena-Niculina Dragoi; Masoud Moradi

doi:10.1021/acsomega.9b03518

Modeling the Interfacial Tension of Water-Based Binary and Ternary Systems at High Pressures Using a Neuro-Evolutive Technique

ACS Omega. 2019 Dec 24;5(1):781-790. doi: 10.1021/acsomega.9b03518. eCollection 2020 Jan 14.

Authors

Yasser Vasseghian¹, Alireza Bahadori², Alireza Khataee^{3

4

5}, Elena-Niculina Dragoi⁶, Masoud Moradi¹

Affiliations

¹ Research Center for Environmental Determinants of Health (RCEDH), Health Institute, Kermanshah University of Medical Sciences, 6715847141 Kermanshah, Iran.
² School of Environment, Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia.
³ Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, 51666-16471 Tabriz, Iran.
⁴ Department of Environmental Engineering, Gebze Technical University, 41400 Gebze, Turkey.
⁵ Institute of Research and Development, Duy Tan University, 550000 Da Nang, Vietnam.
⁶ Faculty of Chemical Engineering and Environmental Protection "Cristofor Simionescu", "Gheorghe Asachi" Technical University, Bld Mangeron no 73, 700050 Iasi, Romania.

Abstract

In this study, artificial neural networks (ANNs) determined by a neuro-evolutionary approach combining differential evolution (DE) and clonal selection (CS) are applied for estimating interfacial tension (IFT) in water-based binary and ternary systems at high pressures. To develop the optimal model, a total of 576 sets of experimental data for water-based binary and ternary systems at high pressures were acquired. The IFT was modeled as a function of different independent parameters including pressure, temperature, density difference, and various components of the system. The results (total mean absolute error of 3.34% and a coefficient of correlation of 0.999) suggest that our model outperforms other habitual models on the ability to predict IFT, leading to a more accurate estimation of this important feature of the gas mixing/water systems.