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

Abulhassan Ali, Aymn Abdulrahman, Sahil Garg, Khuram Maqsood, Ghulam Murshid

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalGreenhouse Gases: Science and Technology
DOIs
Publication statusAccepted/In press - Jan 1 2018

Keywords

  • artificial neural network
  • CO freezing prediction
  • CO-CH phase equilibria
  • cryogenic CO separation
  • solid CO formation

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry

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