Digital Library of the European Council for Modelling and Simulation

Title:	Application Of Artificial Neural Networks In Prediction Of Vapour Liquid Equilibrium Data
Authors:	Manish Vashishtha
Published in:	(2011).ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho. European Council for Modeling and Simulation. doi:10.7148/2011 ISBN: 978-0-9564944-2-9 25^th European Conference on Modelling and Simulation, Jubilee Conference Krakow, June 7-10, 2011
Citation format:	Vashishtha, M. (2011). Application Of Artificial Neural Networks In Prediction Of Vapour Liquid Equilibrium Data. ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho (pp. 142-145). European Council for Modeling and Simulation. doi:10.7148/2011-0142-0145
DOI:	http://dx.doi.org/10.7148/2011-0142-0145
Abstract:	The associative property of artificial neural networks (ANNs) and their inherent ability to “learn” and “recognize” highly non-linear and complex relationships finds them ideally suited to a wide range of applications in chemical engineering. The present paper deals with the potential applications of ANNs in thermodynamics – particularly, the prediction/ estimation of vapour-liquid equilibrium (VLE) data. The prediction of VLE data by conventional thermodynamic methods is tedious and requires determination of “constants” which is arbitrary in many ways. Also, the use of conventional thermodynamics for predicting VLE data for highly polar substances introduces a large number of inaccuracies. The possibility of applying ANNs for VLE data prediction/ estimation has been explored using the back propagation algorithm Application of ANNs to the VLE predictions of NH3 – H2O and CH4 – C2H6 system is investigated. The results of neural equation of state (NEOS) are compared with popular thermodynamic approaches. The inputs to the net consist of Temperature and Pressure. Liquid and vapour phase compositions are obtained as outputs. These outputs are compared with the predictions obtained by using Peng Robinson equation of state and Wilson activity coefficients. For NH3 – H2O system vapour phase compositions are well predicted by all three approaches but thermodynamic approaches are unable to predict liquid phase compositions. ANNs or NEOS gives good results. For CH4 – C2H6 system, both Peng Robinson EOS and NEOS give good results. From the present work it is concluded that though for simple systems ANNs do not offer additional advantage, they are certainly superior when complex and polar systems are encountered. An heuristic approach to reduce the trial and error process for selecting the “optimum” net architecture is discussed.
Full text: