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

• Published in: Greenhouse gases: science and technology 2019-02, Vol.9 (1), p.67-78
• Main Authors: Ali, Abulhassan, Abdulrahman, Aymn, Garg, Sahil, Maqsood, Khuram, Murshid, Ghulam
• Format: Article
• Language: English
• Subjects: artificial neural network; Artificial neural networks; Carbon dioxide; CO2 freezing prediction; CO2‐CH4 phase equilibria; cryogenic CO2 separation; Data points; Data processing; Energy conservation; Experimental data; Mathematical models; Methane; Natural gas; Neural networks; Organic chemistry; Prediction models; Purification; solid CO2 formation; Vapors
• ISSN: 2152-3878; 2152-3878
• DOI: 10.1002/ghg.1833

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. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.