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Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent System.
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- Molecules, 2021, v. 26, n. 1, p. 156, doi. 10.3390/molecules26010156
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Mathematical Model of the Phase Diagrams of Ionic Liquids-Based Aqueous Two-Phase Systems Using the Group Method of Data Handling and Artificial Neural Networks.
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- Theoretical Foundations of Chemical Engineering, 2018, v. 52, n. 1, p. 146, doi. 10.1134/S0040579518010165
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- Article
On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling.
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- Water Environment Research (10614303), 2024, v. 96, n. 1, p. 1, doi. 10.1002/wer.10960
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- Article
Predicting hydrogen storage capacity of metal–organic frameworks using group method of data handling.
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- Neural Computing & Applications, 2020, v. 32, n. 18, p. 14851, doi. 10.1007/s00521-020-04837-3
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Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state.
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- Scientific Reports, 2021, v. 11, n. 1, p. 1, doi. 10.1038/s41598-021-97131-8
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Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-39079-5
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- Article
Modeling of H<sub>2</sub>S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-34193-w
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Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state.
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- Scientific Reports, 2022, v. 12, n. 1, p. 1, doi. 10.1038/s41598-022-18983-2
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- Article
Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches.
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- Scientific Reports, 2022, v. 12, n. 1, p. 1, doi. 10.1038/s41598-022-17983-6
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Modeling of nitrogen solubility in normal alkanes using machine learning methods compared with cubic and PC-SAFT equations of state.
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- Scientific Reports, 2021, v. 11, n. 1, p. 1, doi. 10.1038/s41598-021-03643-8
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- Article
Toward empirical correlations for estimating the specific heat capacity of nanofluids utilizing GRG, GP, GEP, and GMDH.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-47327-x
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