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- W3198576770 abstract "- Nitrous oxide (N2O), as a potent greenhouse gas, is increasingly becoming a major multidisciplinary concern in recent years. Therefore, the removal of N2O using powerful green solvents such as ionic liquids (ILs) has turned into an attractive way to reduce the amount of N2O in the atmosphere. -The aim of this study was to establish rigorous models that can predict the solubility of N2O in various ILs. To achieve this, three advanced soft-computing methods, viz. cascaded forward neural network (CFNN), radial basis function neural network (RBFNN), and gene expression programming (GEP) were trained and tested using comprehensive experimental measurements. - The obtained results demonstrated that the newly implemented models can predict the solubility of N2O in ILs with high accuracy. Besides, it was found that the CFNN model optimized using Levenberg-Marquardt (LM) algorithm was the best predictive paradigm (R2=0.9994 and RMSE=0.0047). Lastly, the Leverage technique was carried out, and the statistical validity of the newly implemented model was documented as more than 96% of data were located in the applicability realm of this paradigm." @default.
- W3198576770 created "2021-09-13" @default.
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- W3198576770 date "2021-11-01" @default.
- W3198576770 modified "2023-10-02" @default.
- W3198576770 title "Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming" @default.
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- W3198576770 doi "https://doi.org/10.1016/j.jtice.2021.08.042" @default.
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