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

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.

SUBMITTER: Vasseghian Y

PROVIDER: S-EPMC6964515 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Vasseghian Yasser Y Bahadori Alireza A Khataee Alireza A Dragoi Elena-Niculina EN Moradi Masoud M

ACS omega 20191224 1

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 pr ...[more]

PMID: 31956829

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