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Prediction of Vapour-Liquid Equilibrium Data Using Neural Network for Hydrocarbon Ternary System. ethane-propane-n-butane
Research article
  

Prediction of Vapour-Liquid Equilibrium Data Using Neural Network for Hydrocarbon Ternary System. ethane-propane-n-butane


I. A.Daniyan , A. O.Adeodu , O. L.Daniyan

1. Department of Mechanical &Mechatronics Engineering, Afe Babalola University.
2. Centre for Basic Space Science, University of Nigeria.


Corresponding author :

I. A. Daniyan
Department of Mechanical &Mechatronics Engineering,
Afe Babalola University,
Ado Ekiti, Nigeria.

Received: January 6, 2014,   Accepted: January 14, 2014,   Published: January 16, 2014.


Abstract:

The prediction of vapour- liquid equilibrium is useful in process simulation and control as well making process engineering design decisions. Prediction of vapour-liquid equilibrium data was carried out using MATLAB software. Pre-existing data of hydrocarbon ternary system (ethane-propane-n-butane) in terms of phase composition, temperature and pressure was trained by iteratively adjusting networks, initializing weights and biases to minimize the network performance function net. MATLAB a software package containing artificial neural network was employed to predict the point where there is no change in composition of both liquid and vapour formed when liquid mixtures of ethane-propane-n-butane vapourises. Predicted values show reasonable and good correlation results when compared to the experimental data thus indicating that the network is an efficient and a good prediction tool for vapour-liquid equilibrium ternary systems.


Keywords: artificial neural network, biases, correlation, performance function net, vapour-liquid


Citation:

I. A. Daniyan(2014) Prediction of Vapour-Liquid Equilibrium Data Using Neural Network for Hydrocarbon Ternary System (ethane-propane-n-butane). J. of Computation in Biosciences and Engineering. V1I1. DOI: 10.15297/JCLS.V1I1.03


Copyright:

© 2014 I. A. Daniyan, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


      
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      Journal of Computation in Biosciences and Engineering