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Titre: Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming
Auteur(s): Nait Amar, Menad
Ghriga, MMohammed Abdelfetah
Ben Seghier, Mohamed El Amine
Ouaer, Hocine
Mots-clés: Nitrous oxide
Ionic liquids
Solubility
Data-driven
Greenhouse gas
Date de publication: 2021
Editeur: Elsevier
Collection/Numéro: Journal of the Taiwan Institute of Chemical Engineers/ (2021);
Résumé: Background: - 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. Methods: -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. Significant Findings: - 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. © 2021 Taiwan Institute of Chemical Engineers
URI/URL: https://doi.org/10.1016/j.jtice.2021.08.042
https://www.sciencedirect.com/science/article/abs/pii/S1876107021005162
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7101
ISSN: 1876-1070
Collection(s) :Publications Internationales

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