شماره ركورد كنفرانس :
5048
عنوان مقاله :
Application of GMDH-type neural network to prediction of liquid-liquid equilibrium for system water + ethanol + trans-decalin
Author/Authors :
Shahbaz ،Zahr Reyhani Faculty of Engineering - University of Guilan - Rasht, Iran , Hossein ،Ghanadzadeh Faculty of Engineering - University of Guilan - Rasht, Iran
كليدواژه :
Liquid–Liquid Equilibrium , Ternary System , Artificial Neural Network , Group Method of Data Handling
عنوان كنفرانس :
ششمين كنگره بين المللي مهندسي شيمي
چكيده لاتين :
In recent years, the traditional neural networks are used to model LLE, but the performance of them related to their
topologies. This paper presents the Group Method of Data Handling (GMDH) technique to estimate LLE data of a
ternary system (water + ethanol + trans-decalin) at 300.2-315.2 K. The GMDH-type neural networks are selforganizing
networks that the number of nodes and layers of them are adjusted during training process. The phase
diagrams for the ternary mixtures including both the experimental and estimated tie-lines are presented. The results
compared in terms of root mean square deviations between experimental and calculated values which were obtained
using the GMDH and UNIFAC models.
