شماره ركورد كنفرانس :
5048
عنوان مقاله :
Application of adaptive neuro-fuzzy inference system for estimation of vapor+ liquid equuilibria of binary systems, carbon dioxide–ethyl caproate, ethyl caprylate and ethyl caprate
Author/Authors :
mojtaba ،hoseini nasab Department of chemical engineerin - Tarbiat Modares University - Gisha bridge - Tehran, Iran , Amir Abbas ،Izadpanah Department of chemical engineerin - Khalij Fars University - Booshehr, Iran , Mohsen ،Vafaei Department of chemical engineerin - Tarbiat Modares University - Gisha bridge - Tehran, Iran
كليدواژه :
Equilibrium , Liquid-vapor , Adaptive neuro-fuzzy inference system , binary mixture
عنوان كنفرانس :
ششمين كنگره بين المللي مهندسي شيمي
چكيده لاتين :
Vapor–liquid equilibria (VLE) play a vital role in designing and modelling 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. adaptive neuro-fuzzy inference
system (ANFIS) is presented for the simultaneous estimation of vapor-liquid equilibria (VLE) of three binary systems
viz. carbon dioxide–ethyl caproate, ethyl caprylate and ethyl caprate which are of importance in supercritical extraction.
The root mean square errors (RMSE) and determination coefficient (R2) performance functions are used to evaluate the
adequacy of the models. The results obtained in this work indicate that ANFIS have better agreement with experimental
data than the thermodynamics models.
