Using the Experimental Cross-Association Energy and Artificial Neural Network for Modeling the Phase Equilibrium of Carbon Dioxide–Water System: What Advances Can Be Achieved?

Autor: Rahmani, Zahra, Davani, Ehsan, Khosharay, Shahin
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Zdroj: International Journal of Thermophysics; Feb2024, Vol. 45 Issue 2, p1-27, 27p
Abstrakt: In this work, first, the phase equilibrium of the CO2/water system has been conducted by using two equations of state, including Two-State and perturbed chain statistical association fluid theory equations of state. The experimental value of cross-association energy (exists in the previous studies) is used for this study to model the equilibrium composition CO2 in the aqueous phase. Two strategies are applied to carbon dioxide. In the first strategy, carbon dioxide can only have cross-association with water, so it has no self-association. The second strategy considers CO2 as both self and cross-associating fluid. Also, an additional equation is used for the cross-association section which reduces the number of adjustable parameters. The results of this study show that the first strategy is successful for all cases, and it is accurate while the second strategy is unsuccessful for Two-State equation of state. Moreover, the application of the first strategy and experimental cross-association energy makes the model independent of simplicity of the model and number of adjustable parameters. Then with the sufficient amount of datasets, machine learning techniques were applied to predict the solubility of CO2 in the water with high accuracy. The results are in good agreement with the experimental data with the correlation coefficient (R) of 0.999 and mean-square root of 4.45e−6 for multilayer perceptron network, which means that the network can predict the solubility for the wide range of temperature and pressure. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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