Exploring CO2 solubility in 1-N-butyl-3-methylimidazolium hexafluorophosphate ionic liquid using neural network models

In this work, [Bmim][PF₆] ionic liquid (i.e., 1-N-butyl-3-methylimidazolium hexafluorophosphate) is utilized for CO₂ capture. Since the experimental studies have their difficulty, we tried to develop multi-layer perceptron (MLP) and radial basis function (RBF) neural network models for estimating CO₂ mole fraction in [Bmim][PF₆]. The MLP with 11 hidden neurons, tangent and logarithm sigmoid activation functions in the secret and output layers, trained by the Levenberg-Marquardt algorithm, is selected as the most accurate neural network model. In the first layer, the weight matrix of temperature and pressure has been determined at 23.1182 and 2.9099, respectively. The bias vector is calculated at 15.544 in the first layer. In the second layer, the values of the weight vector and bias were determined at - 1.2246 and - 24.1089, respectively. It is worth noting that the MLP model provides the relative deviation of 0.0859%, 8.74%, and 30.88% for temperatures of 283.15, 298.15, and 323.15 K, respectively. The results confirmed that the MLP neural network presents higher inaccuracy for predicting CO₂ solubility at higher temperatures.