Artificial neural network for the second virial coefficient of organic and inorganic compounds: An ANN for B of organic and inorganic compounds

ABSTRACT

An artificial neural network (ANN) to estimate the second virial coefficient, valid for organic and inorganic compounds, is presented. First, we statistically analyzed 6,531 experimental points, belonging to 234 fluids, collected from literature. The data were investigated with a factor analysis approach to identify the most significant parameters that influence the second virial coefficient. The factor analysis, combined with physical considerations, allowed to find four (T_r, T_c, P_c, ω) or five (μ_r) parameters as input variables for the ANN, according to the specific chemical family. The architecture of the proposed multi-layers perceptron (MLP) neural network consists of one input layer with five input variables (T_r, T_c, P_c, ω, μ_r), one output layer with one neuron (B) and two-hidden-layers with 19 neurons each. We trained, validated and tested several configurations of the neural network to obtain this network topology that minimizes the deviations between experimental and calculated points. Results show that the ANN is able to calculate the second virial coefficient with greater accuracy (RMSE = 29.38 cm³/mol) than that of correlations available in literature. To identify the outliers and applicability domain of the proposed MLP neural network, an outlier diagnosis based on the Leverage approach was performed. This analysis shows that the model is statistically valid.

KEYWORDS:

• Bootstrapping
• correlation matrix
• factor analysis
• feedforward networks
• k-fold cross-validation
• virial EoS
• Williams plot