Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids

Applied Sciences

Shahaboddin Shamshirband¹  PålØstebø Andersen²  Mohamed El Amine Ben Seghier³  Menad Nait Amar⁴  Narjes Nabipour⁵  Amir Mosavi⁶  Hocine Ouaer⁷  MohammedAbdelfetah Ghriga⁷  AmirHossein Hosseini⁸ 

[1] Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam;Department of Energy Resources, University of Stavanger, 4036 Stavanger, Norway;Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 729000, Vietnam;Département Etudes Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes 35000, Algeria;Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany;Laboratoire Génie Physique des Hydrocarbures, Faculté des Hydrocarbures et de la Chimie, Université M’Hamed Bougara de Boumerdes, Avenue de l’Indépendance, Boumerdes 35000, Algeria;Petroleum Department, Semnan University, Semnan 3513119111, Iran;

关键词: co₂ solubility;    ionic liquids;    carbon dioxide;    multilayer perceptron;    gene expression programming;    prediction;    equation of state;    machine learning;   

DOI  :  10.3390/app10010304

来源: DOAJ

【 摘 要 】

Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO₂) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg−Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng−Robinson (PR) or Soave−Redlich−Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R² = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R² from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R² = 0.9896 and RMSE = 0.0201.

【 授权许可】

Unknown