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

Corresponding Author : I. A. Daniyan

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

Email ID : afolabiilesanmi@yahoo.com

Received : 2014-01-06 Accepted : 2014-01-14 Published : 2014-01-14

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 : 10.5281/zenodo.999784

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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