Estimation of vapour liquid equilibria of binary systems, carbon dioxide–ethyl caproate, ethyl caprylate and ethyl caprate using artificial neural networks

Vapour liquid equilibrium (VLE) data are important for designing and modeling of process equipments. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations of state are used for estimation of VLE. In this paper, an alternate tool, i.e. the artificial neural network technique has been applied for estimation of VLE for three binary systems viz. carbon dioxide–ethyl caproate, ethyl caprylate and ethyl caprate which are of importance in supercritical extraction. The temperature range in which these models are valid is 308.2–328.2 K and the pressure range is 1.6–9.2 MPa. The average absolute deviation for all the three systems in the estimation of liquid phase mole fraction was 3% or less and less than 0.02% for the vapour phase mole fraction. The error was less compared to that estimated by SRK or Peng Robinsons equation of state.