Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model
Abstract This study presents a robust method for predicting CO2 solubility in Deep Eutectic Solvents (DESs) using the stochastic gradient boosting (SGB) algorithm. DESs, promising green solvents for CO2 capture, require precise solubility data for practical applications in industrial and environment...
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04318-4 |
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| author | Reza Soleimani Amir Hossein Saeedi Dehaghani Ziba Behtouei Hamidreza Farahani Seyyed Mohsen Hashemi |
| author_facet | Reza Soleimani Amir Hossein Saeedi Dehaghani Ziba Behtouei Hamidreza Farahani Seyyed Mohsen Hashemi |
| author_sort | Reza Soleimani |
| collection | DOAJ |
| description | Abstract This study presents a robust method for predicting CO2 solubility in Deep Eutectic Solvents (DESs) using the stochastic gradient boosting (SGB) algorithm. DESs, promising green solvents for CO2 capture, require precise solubility data for practical applications in industrial and environmental settings. The model incorporates key parameters such as temperature, pressure, mole percent of salt and hydrogen bond donor (HBD) compounds, HBD melting points, molecular weights of salts and HBDs, and other critical factors. Using a dataset of 1951 experimental data points spanning temperatures (293.15–343.15 K) and pressures (26.3–12,730 kPa), the SGB model demonstrated excellent predictive accuracy, achieving an R2 of 0.9928 and an AARD% of 2.3107. Variable importance analysis identified pressure as the most influential factor. The model’s applicability, confirmed through William’s plot, encompassed 97.5% of data points within a safety margin, ensuring reliability, versatility, and broad applicability. Moreover, the SGB model outperformed previous methods, including ANN, RF, and thermodynamic models like PR-EoS and COSMO-RS, as validated by statistical metrics. This research highlights the SGB model’s potential as a superior and practical tool for evaluating CO2 solubility in DESs, advancing the field of green solvent development for sustainable and efficient CO2 capture technologies. |
| format | Article |
| id | doaj-art-2b05ddf1f63c43bf91482d0e307b2193 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-2b05ddf1f63c43bf91482d0e307b21932025-08-20T02:05:46ZengNature PortfolioScientific Reports2045-23222025-06-0115111910.1038/s41598-025-04318-4Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree modelReza Soleimani0Amir Hossein Saeedi Dehaghani1Ziba Behtouei2Hamidreza Farahani3Seyyed Mohsen Hashemi4Department of Chemical Engineering, Faculty of Chemical Engineering, Tarbiat Modares UniversityDepartment of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares UniversitySchool of Computing and Informatics, University of Louisiana at LafayetteDepartment of Computer Engineering, Damavand Branch, Islamic Azad UniversityDepartment of Computer Engineering, Science and Research Branch, Islamic Azad UniversityAbstract This study presents a robust method for predicting CO2 solubility in Deep Eutectic Solvents (DESs) using the stochastic gradient boosting (SGB) algorithm. DESs, promising green solvents for CO2 capture, require precise solubility data for practical applications in industrial and environmental settings. The model incorporates key parameters such as temperature, pressure, mole percent of salt and hydrogen bond donor (HBD) compounds, HBD melting points, molecular weights of salts and HBDs, and other critical factors. Using a dataset of 1951 experimental data points spanning temperatures (293.15–343.15 K) and pressures (26.3–12,730 kPa), the SGB model demonstrated excellent predictive accuracy, achieving an R2 of 0.9928 and an AARD% of 2.3107. Variable importance analysis identified pressure as the most influential factor. The model’s applicability, confirmed through William’s plot, encompassed 97.5% of data points within a safety margin, ensuring reliability, versatility, and broad applicability. Moreover, the SGB model outperformed previous methods, including ANN, RF, and thermodynamic models like PR-EoS and COSMO-RS, as validated by statistical metrics. This research highlights the SGB model’s potential as a superior and practical tool for evaluating CO2 solubility in DESs, advancing the field of green solvent development for sustainable and efficient CO2 capture technologies.https://doi.org/10.1038/s41598-025-04318-4Stochastic gradient boostingCO2 captureDeep eutectic solventsSolubilityPrediction |
| spellingShingle | Reza Soleimani Amir Hossein Saeedi Dehaghani Ziba Behtouei Hamidreza Farahani Seyyed Mohsen Hashemi Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model Scientific Reports Stochastic gradient boosting CO2 capture Deep eutectic solvents Solubility Prediction |
| title | Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model |
| title_full | Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model |
| title_fullStr | Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model |
| title_full_unstemmed | Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model |
| title_short | Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model |
| title_sort | prognostication of advanced co2 capture using tunable solvents with an ensemble learning based decision tree model |
| topic | Stochastic gradient boosting CO2 capture Deep eutectic solvents Solubility Prediction |
| url | https://doi.org/10.1038/s41598-025-04318-4 |
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