Application of artificial neural networks (ANN) for vapor-liquid-solid equilibrium prediction for CH₄-CO₂ binary mixture

The study of the frosting behavior of CO₂ in the binary CH₄-CO₂ is very important for energy minimization and for the smooth operation of the cryogenic purification process for natural gas due to its extensive cooling requirements. The present study focuses on the solid region of the phase envelope and the development of a predictive model using the artificial neural network (ANN) technique. It validates the model using available experimental data. The model points out the outlying data points. The ANN prediction method developed in this work can be successfully used for the vapor-solid (V-S) and vapor-liquid-solid (V-L-S) equilibrium of a CH₄-CO₂ binary mixture for CO₂ concentration of 1 to 54.2% and a temperature range of −50°C to −200°C. The use of the model for the liquid-solid (L-S) region in its current form is not recommended because the model was not validated due to lack of experimental data in this region.