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.

Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review

“…For training: (i) the algorithm was Adam, a stochastic gradient descent method that uses adaptive estimations of first and second-order moments, (ii) the number of epochs was set to 30, (iii) the batch size was set 10, (iv) the validation data were set at 10% from the training data, and (v) the shuffling training data were set to true [34]. Before starting the program, the dataset was loaded into memory, shuffled, and split into training (80%), validation (10% from training), and testing (10%) [35,36].…”

Comparative Analysis of Four Neural Network Models on the Estimation of CO2–Brine Interfacial Tension

“…Yasser et al reviewed the application of square gradient theory, linear gradient theory, density functional theory, drop shape analysis, axisymmetric drop-shape analysis, density gradient theory, and other methods for the prediction of IFT. They collected 576 data sets and established water-based binary and ternary (CO 2 , CH 4 , and N 2 ) IFT models under elevated pressure by using neuroevolutionary technology.…”

“…Many researchers try to find a reliable and convenient method to predict the IFT under various conditions. Zhang et al 4 summarized the prediction models of Hebach et al, 14 Chalbaud et al, 11 and Li et al 15,17 32 reviewed the application of square gradient theory, linear gradient theory, density functional theory, drop shape analysis, axisymmetric drop-shape analysis, density gradient theory, and other methods for the prediction of IFT. They collected 576 data sets and established water-based binary and ternary (CO 2 , CH 4 , and N 2 ) IFT models under elevated pressure by using neuroevolutionary technology.…”

Generating a Machine-Learned Equation of State for Fluid Properties

“…A review of some of the first applications of ANNs to the chemical sciences and engineering was provided by Himmelblau . In addition, ANNs have been used to selectively correlate a limited number of thermophysical properties of restricted families of compounds, for example, alkanes, , ionic liquids, refrigerants, , components of biofuels, and gases. , Critical properties, interfacial properties, and partition coefficients , have all been individually explored. The reader is referred to an excellent recent review by Forte et al and references therein for a modern account.…”