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Reseach Article

Developing Optimum ANN Model for Mass Transfer with Chemical Reaction in Packed Column for Air-Carbon Dioxide and Aqueous Sodium Hydroxide System

by S. L. Pandharipande, Ankit Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 7
Year of Publication: 2013
Authors: S. L. Pandharipande, Ankit Singh
10.5120/10093-4727

S. L. Pandharipande, Ankit Singh . Developing Optimum ANN Model for Mass Transfer with Chemical Reaction in Packed Column for Air-Carbon Dioxide and Aqueous Sodium Hydroxide System. International Journal of Computer Applications. 62, 7 ( January 2013), 17-20. DOI=10.5120/10093-4727

@article{ 10.5120/10093-4727,
author = { S. L. Pandharipande, Ankit Singh },
title = { Developing Optimum ANN Model for Mass Transfer with Chemical Reaction in Packed Column for Air-Carbon Dioxide and Aqueous Sodium Hydroxide System },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 7 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number7/10093-4727/ },
doi = { 10.5120/10093-4727 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:08.383228+05:30
%A S. L. Pandharipande
%A Ankit Singh
%T Developing Optimum ANN Model for Mass Transfer with Chemical Reaction in Packed Column for Air-Carbon Dioxide and Aqueous Sodium Hydroxide System
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 7
%P 17-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gas-liquid reactions occupy a large share of the chemical reactions that are carried out industrially. The estimation of reaction rate is the most primary but complex task and is a prerequisite in design & scaling up of reactors. Reaction rate is a function of number of parameters that include flow rates of gas and liquid phases, concentration of reactants and interfacial surface area, among others. Artificial Neural Network (ANN) is viewed as a black box modeling tool and has been applied for modeling of several chemical engineering operations. The present work is aimed at developing artificial neural network model for estimation of interfacial area as a function of gas & liquid flow rates and inlet concentrations of gas & liquid phases. Fifteen ANN models with different topologies have been developed and three models S-20, M-20 and C-50 are shortlisted based on better RMSE values. Further, model C-50 has been observed to be highly acceptable based on comparison of relative error for all the data points.

References
  1. G. M. Richards, G. A. Ratcliff, P. V. Danckwerts, Chemical Engineering Science 19 (1964) 325.
  2. A. S. Jhaveri, M. M. Sharma, Chemical Engineering Science 23 (1968) 669.
  3. G. E. H. Joosten, P. V. Danckwerts, Chemical Engineering Science 28 (1973) 453.
  4. B. N. Sahay, M. M. Sharma, Chemical Engineering Science 28 (1973) 41.
  5. K. Sridharan, M. M. Sharma, Chemical Engineering Science 31 (1976) 767.
  6. V. A. Juvekar, M. M. Sharma, Chemical Engineering Science, 28 (1973) 976.
  7. M. H. Oyevaar, K. R. Westerterp, Chemical Engineering Science , 44 (1989) 2691.
  8. L. De Leye, G. F. Fromen, Computers and Chemical Engineering, 10 (1986) 493.
  9. Nikolett Sipöcza, Finn Andrew Tobiesenb, Mohsen Assadia, Applied Energy 88 (2011) 2368.
  10. Jerzy Ma?kowiak, Chemical Engineering Research and Design 89 (2011) 1308.
  11. H. Hikita, S. Asai, T. Takatsuka, The Chemical Engineering Journal 11 (1976) 131.
  12. Adisorn Aroonwilas, Amit Chakma, Paitoon Tontiwachwuthikul, Amornvadee Veawab, Chemical Engineering Science 58 (2003) 4037.
  13. Anderson J. A. An Introduction to Neural Networks. New Delhi: Prentice-Hall of India, Pvt Ltd; 1999.
  14. Belsito S, Banerjee S. AIChEJ 44 (1998) 2675.
  15. Baratti R, Vacca G, Servida A. 74 (1995) 35.
  16. Fan J. Y, Nikolau M, White R. E. AIChEJ 39 (1993) 82.
  17. Hoskins J. C, Kaliyur K. M, Himmelblau D. M. AIChEJ, 37 (1991) 137.
  18. Pandharipande S. L. An Introduction to Artificial Neural Networks. Nagpur: Denett Publications; 2008.
  19. Pandharipande S. L, Badhe Y. P. Chem Eng World, 38 (2003) 70.
  20. Pandharipande S. L, Badhe Y. P. Chem Eng World 38 (2003) 82.
  21. Pandharipande S. L, Badhe Y. P. IChE, 45 (2003) 256.
  22. Pandharipande S. L, Agarwal R. S, Gogte B. B, Badhe Y. P. Chem Eng World, 38 (2003) 78.
  23. Pandharipande S. L, Mandavgane S. A. Indian J Chem Technol, 11 (2004) 820.
  24. Pandharipande S. L, Bhaise A. , Poharkar A. Chem Eng world, 39 (2004) 50.
  25. Pandharipande S. L, Badhe Y. P. J Inst Eng, 84 (2004) 65.
  26. Pandharipande S. L, Badhe Y. P, 2004. elite-ANN©, ROC No SW-1471.
  27. Pandharipande S L, Shah A M, Heena T, International Journal of Computer Applications 41(2012) 23.
  28. Pandharipande S L, Khonde R D, International Journal of Computer Applications 41(2012)
  29. Rumelhart D. E, McClleland, Back Propagation Training Algorithm Processing, M. I. T Press, 1986 Cambridge Massachusetts.
  30. Watanabe K. , Abe M. , Kubota M. , Himmelblau D. M. AIChEJ, 35 (1989) 137.
  31. Zamankhan P. , Malinen P. , Lepomaki H. AIChEJ, 43 (1997) 1684.
Index Terms

Computer Science
Information Sciences

Keywords

Developing Optimum