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

Hocine Ouaer,Pål Østebø Andersen,Mohammed Abdelfetah Ghriga,Menad Nait Amar,Shahaboddin Shamshirband,Narjes Nabipour,Amir Mosavi,Amir Hossein Hosseini,Mohamed El Amine Ben Seghier

doi:10.3390/app10010304

Abstract

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.

Highlights

The natural gas produced from the subterranean gas fields and subsequently transported through pipelines should meet certain specifications such as environmental and safety standards as well as those of sale gas sectors
The mean square error (MSE) was used as the assessment criterion, which is defined mathematically as follows: Pn xiexp − xipre n where x stands for mole fraction of CO2, exp and pred indicate the experimental and the predicted values, respectively, and n represents the number of samples
The model tuning parameters giving the lowest MSE on the training set were considered the choice for the trained model

Summary

Introduction

The natural gas produced from the subterranean gas fields and subsequently transported through pipelines should meet certain specifications such as environmental and safety standards as well as those of sale gas sectors. The products destined for sale should be free of undesirable contaminants, e.g., carbon dioxide (CO2 ) and hydrogen sulfide (H2 S), which are both toxic and unfriendly from an environmental point of view. Several treatments are employed to remove acidic gases from natural gas. The most famous are the alkanolamine-based treatment operations [1,2,3]. This technique was firstly introduced for carbon dioxide removal in 1991 [4,5]. MEA was the most preferred alkanolamine compared to DEA and TEA because of its reactivity, low molecular weight, and lower required circulation to maintain a given amine to acid-gas mole ratio

Methods

Results

Conclusion