Modeling of CO2 capture ability of [Bmim][BF4] ionic liquid using connectionist smart paradigms

The burning of fossil fuels produces large amounts of exhaust gases containing carbon dioxide (CO2). The emission of CO2 into the atmosphere is widely known as the leading cause of global warming and climate change. The separation processes are responsible for capturing the CO2 to reduce its undesirable effects on the environment. Since the conventional processes have their drawbacks, it is crucial to find a more environment-friendly process for CO2 capture. Recently, ionic liquids (ILs) have become an interesting candidate for CO2 capture. In this study, the solubility of CO2 in the 1-n-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) is estimated using six different artificial intelligence (AI) techniques, including four artificial neural networks (ANN), support vector machines (LS-SVM), adaptive neuro-fuzzy interface system (ANFIS). The cascade feed-forward neural network has been found as the best model for the considered matter. This model predicts overall experimental datasets with excellent accuracy of AARD = 6.88%, MSE = 8 x 10(-4), and R-2 = 0.98808. The maximum mole fraction of CO2 in the ionic liquid (i.e., 0.8) can be obtained at the highest pressure and the lowest temperature. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study estimates the solubility of CO2 in ionic liquid using various artificial intelligence techniques, with the cascade feed-forward neural network identified as the best model. This model accurately predicts the experimental data, showing that the maximum mole fraction of CO2 can be obtained at the highest pressure and the lowest temperature.