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Reseach Article

Artificial Neural Network Modeling of Equilibrium Relationship for Partially Miscible Liquid-Liquid Ternary System

by S. L. Pandharipande, Yogesh Moharkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 8
Year of Publication: 2012
Authors: S. L. Pandharipande, Yogesh Moharkar
10.5120/8219-1641

S. L. Pandharipande, Yogesh Moharkar . Artificial Neural Network Modeling of Equilibrium Relationship for Partially Miscible Liquid-Liquid Ternary System. International Journal of Computer Applications. 52, 8 ( August 2012), 1-5. DOI=10.5120/8219-1641

@article{ 10.5120/8219-1641,
author = { S. L. Pandharipande, Yogesh Moharkar },
title = { Artificial Neural Network Modeling of Equilibrium Relationship for Partially Miscible Liquid-Liquid Ternary System },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 8 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number8/8219-1641/ },
doi = { 10.5120/8219-1641 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:42.892326+05:30
%A S. L. Pandharipande
%A Yogesh Moharkar
%T Artificial Neural Network Modeling of Equilibrium Relationship for Partially Miscible Liquid-Liquid Ternary System
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 8
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The equilibrium relationship for a ternary mixture containing one pair of partially miscible components can be expressed in the form of a ternary diagram depicting a binodal curve. Depending upon the location of the point representing the composition of the mixture, the ternary diagram may be divided into three parts 1, 2 & 3, for, whether the point is on the Binodal curve, single phase region or two phase region, respectively. Because of this unique nature of equilibrium relationship modeling of such partially miscible ternary system becomes very complex. Artificial neural network (ANN) is an upcoming modeling tool & has high accuracy levels even for processes involving multivariable non-linear relationships. The objective of the present work is to develop ANN models for the system of acetic acid-water-benzene for prediction of type of resulting mixture whether single homogeneous liquid phase, two immiscible liquid phases or a single equilibrium liquid phase of the pair of partially miscible system. The equilibrium data generated experimentally has been used. Different topology of ANN architecture has been tried for model 1 & 2. Selection of ANN model is based on the comparison of % relative error for predicted output values for ANN model-1 & 2. The highlight of the present work is the successful incorporation of the linguistics variables in a model with coded values.

References
  1. Coulson and Richardson. 2002. chemical engineering volume-2 fifth edition- Particle Technology and Separation Processes, pp. 721.
  2. Anderson, J. A. 1999. "An Introduction to Neural Networks (Prentice-Hall of India, Pvt. Ltd New Delhi),"
  3. Rumelhart, D. E. and McClleland, 1986. "Back Propagation Training Algorithm Processing," M. I. T Press, Cambridge Massachusetts.
  4. Fan, J. Y. Nikolau M. & White, R. E. 1993. "An approach to Fault diagnosis of chemical processes in Neural networks," AIChE, 82-88.
  5. Hoskins, J. C. Kaliyur, K. M. & Himmelblau, D. M. 1991 "Fault diagnosis in complex chemical plants using artificial neural network," AIChEJ, 137-141.
  6. Watanabe, K. M. , Matsuura Abe, I. , Kubota, M. and Himmelblau, D. M. 1989 "Incipient fault diagnosis of chemical processes via artificial neural networks," AIChEJ, 1803-1812.
  7. Belsito, S. & Banerjee, S. 1998. "Leak detection in liquefied gas pipelines by Artificial neural networks," AIChEJ, 2675-2688.
  8. Pandharipande, S. L. Badhe, Y. P. 2003. "ANN for leak detection in pipelines. " Chem Eng World, 70-72.
  9. Zamankhan, P. , Malinen, P. , Lepomaki, H. 1997. "Application of neural networks to mass transfer predictions in a fast fluidized bed of fine solids," AIChEJ, 1684-1690.
  10. Baratti, R. , Vacca, G. , Servida, A. 1995. "Neural networks modeling of distillation columns," Hydrocarbon Processing, 35-38.
  11. Pandharipande, S. L. , Agarwal, R. S. , Gogte, B. B. , Badhe, Y. P. , 2003. "Detergent formulation by artificial neural network," Chem Eng World, 78- 80.
  12. Pandharipande, S. L. , Badhe, Y. P. 2003. "Unsteady state heat conduction in semi infinite solids artificial neural networks," Chem Eng World, 82-84.
  13. Pandharipande, S. L. , Badhe, Y. P. 2003. "Prediction of mass transfer coefficient in downflow jet loop reactor using artificial neural network," Indian Chemical Engineer, 256-258.
  14. Pandharipande, S. L. , Mandavgane, S. A. , 2004. "Modeling of packed column using artificial neural networks," Indian J Chem Tech, 820-824.
  15. Pandharipande, S. L. , Bhaise, A. , Poharkar, A. , 2004. "Steam tables: Using Artificial Neural Networks," Chem Eng world, 50-54.
  16. Pandharipande, S. L. , Badhe, Y. P. 2004. "Artificial neural networks for Gurney- Lurie and Heisler Charts," J Inst Eng, 65-70.
  17. Pandharipande, S. L. , Badhe, Y. P. elite-ANN©. 2004; ROC No SW-1471 India.
  18. Yogesh Moharkar, Minor project report submitted for degree of M Tech at Rashtrasant Tukadoji Maharaj Nagpur University.
Index Terms

Computer Science
Information Sciences

Keywords

Partially miscible system binodal curve modeling artificial neural network