Nakhaei-Kohani, Reza and Taslimi-Renani, Ehsan and Hadavimoghaddam, Fahime and Mohammadi, Mohammad-Reza and Hemmati-Sarapardeh, Abdolhossein (2022) Modeling solubility of CO2-N-2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state. Scientific Reports, 12 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-07393-z.
Full text not available from this repository.Abstract
Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO2) and nitrogen (N-2) in water and brine is one of the most controversial challenges in the oil and chemical industries. Although many researches have been conducted on solubility of gases in brine and water, very few researches investigated the solubility of power plant flue gases (CO2-N-2 mixtures) in aqueous solutions. In this study, using six intelligent models, including Random Forest, Decision Tree (DT), Gradient Boosting-Decision Tree (GB-DT), Adaptive Boosting-Decision Tree (AdaBoost-DT), Adaptive Boosting-Support Vector Regression (AdaBoost-SVR), and Gradient Boosting-Support Vector Regression (GB-SVR), the solubility of CO2-N-2 mixtures in water and brine solutions was predicted, and the results were compared with four equations of state (EOSs), including Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), Valderrama-Patel-Teja (VPT), and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT). The results indicate that the Random Forest model with an average absolute percent relative error (AAPRE) value of 2.8% has the best predictions. The GB-SVR and DT models also have good precision with AAPRE values of 6.43% and 7.41%, respectively. For solubility of CO2 present in gaseous mixtures in aqueous systems, the PC-SAFT model, and for solubility of N-2, the VPT EOS had the best results among the EOSs. Also, the sensitivity analysis of input parameters showed that increasing the mole percent of CO2 in gaseous phase, temperature, pressure, and decreasing the ionic strength increase the solubility of CO2-N-2 mixture in water and brine solutions. Another significant issue is that increasing the salinity of brine also has a subtractive effect on the solubility of CO2-N-2 mixture. Finally, the Leverage method proved that the actual data are of excellent quality and the Random Forest approach is quite reliable for determining the solubility of the CO2-N-2 gas mixtures in aqueous systems.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Science and Higher Education of the Russian Federation [Grant No: 075-15-2020-900] |
| Uncontrolled Keywords: | Modeling solubility; Electrolyte systems; Artificial intelligence; Equations of state |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Oct 2023 05:08 |
| Last Modified: | 05 Oct 2023 05:08 |
| URI: | http://eprints.um.edu.my/id/eprint/42993 |
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