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Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS)

ISSN:2141-7016

Article Title: Correlation of Vapour-Liquid Equilibrium Data Using Neural Network for Hydrocarbon Ternary System (Ethane-n-Pentane-n-Hexane)
by I. A. Daniyan, A. O. Adeodu and O. O. Adaramola

Abstract:
Correlation of vapour-liquid equilibrium data for hydrocarbon ternary system (ethane-n-pentane-n-heptane) is very useful in the design decision of separation process equipment such as separation columns, extractors etc. The tool used for the correlation is MATLAB: a very reliable software with adequate neural network conditions such as multi-layer feed forward, back propagation etc. A comprehensive Artificial Neural Network (A N N) training and simulation model and list of pre-existing vapour-liquid equilibrium data for ethane-n-pentane-n-heptane system was employed for this work. Neural network was trained in MATLAB 7.10.0 environment.Several iterations were carried out on the ternary system until the performance goal was met. From the analysis of the output result, regression and iteration graphs when compared with experimental data, artificial neural network offered very small deviation from the target. This confirms conclusively that artificial neural network is a consistent and reliable tool for predicting the vapour-liquid phase equilibrium for binary, ternary and quaternary system. The knowledge of correlation also establishes the basic background required for the understanding of the vapour-liquid phase behaviour of ternary systems which forms the basis of calculations of distillation, extraction and absorption processes etc.
Keywords: artificial neural network, back propagation, correlation, matlab, simulation model ternary system.
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ISSN: 2141-7016

Editor in Chief.

Prof. Gui Yun Tian
Professor of Sensor Technologies
School of Electrical, Electronic and Computer Engineering
University of Newcastle
United Kingdom

 

 

Copyright © Journal of Emerging Trends in Engineering and Applied Sciences 2010