Next Article in Journal
Authentication of an Old Violin by Multianalytical Methods
Previous Article in Journal
Zeolite Synthesis Using Imidazolium Cations as Organic Structure-Directing Agents
Open AccessArticle

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

1
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
2
Petroleum Department, Semnan University, Semnan 3513119111, Iran
3
Département Etudes Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes 35000, Algeria
4
Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 729000, Vietnam
5
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 729000, Vietnam
6
Institut des Sciences Analytiques et de Physico-chimie pour l’Environnement et les Matériaux, IPREM, UMR 5254, CNRS Université de Pau et des Pays de l’Adour/E2S, 2 avenue P. Angot, Technopôle Hélioparc, 64000 Pau, France
7
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
8
Department of Energy Resources, University of Stavanger, 4036 Stavanger, Norway
9
The National IOR Centre of Norway, University of Stavanger, 4036 Stavanger, Norway
10
Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany
11
Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
12
Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Brisbane City, QLD 4059, Australia
13
School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
14
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
15
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 304; https://doi.org/10.3390/app10010304
Received: 16 November 2019 / Revised: 8 December 2019 / Accepted: 23 December 2019 / Published: 31 December 2019
(This article belongs to the Section Environmental and Sustainable Science and Technology)
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. View Full-Text
Keywords: CO2 solubility; ionic liquids; carbon dioxide; multilayer perceptron; gene expression programming; prediction; equation of state; machine learning CO2 solubility; ionic liquids; carbon dioxide; multilayer perceptron; gene expression programming; prediction; equation of state; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Ouaer, H.; Hosseini, A.H.; Nait Amar, M.; El Amine Ben Seghier, M.; Ghriga, M.A.; Nabipour, N.; Andersen, P.Ø.; Mosavi, A.; Shamshirband, S. Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids. Appl. Sci. 2020, 10, 304.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop