Random Articles
Reseach Article
Optimizing Topology in Developing Artificial Neural Network Model for Estimation of Hydrodynamics of Packed Column
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 58 - Number 3 |
| Year of Publication: 2012 |
| Authors: S. L. Pandharipande, Ankit Singh |
10.5120/9266-3445
|
S. L. Pandharipande, Ankit Singh . Optimizing Topology in Developing Artificial Neural Network Model for Estimation of Hydrodynamics of Packed Column. International Journal of Computer Applications. 58, 3 ( November 2012), 49-53. DOI=10.5120/9266-3445
Abstract
Different types of packing materials are used to increase gas-liquid contact area in packed columns. The hydrodynamic study of packed column includes the variation of pressure drop and liquid hold up as a function of liquid and gas flow rates. Artificial neural network is an upcoming modeling tool & present work is aimed at optimizing the topology of artificial neural network model for estimation of pressure drop, minimum liquid wetting flow rate and flooding velocity as a function of type and size of packing, liquid flow rate and gas flow rate. The linguistic variable for five types and ten sizes of packing is also incorporated as input parameters with appropriate codes assigned. ANN models with fifteen different topologies have been developed and three models S-50, M-100 and C-10 are shortlisted based on better RMSE values. Further, model M-100 has been observed to be highly acceptable based on comparison of relative error all the data points.
References
- Anderson J A, An Introduction to Neural Networks, New Delhi: Prentice-Hall of India, Pvt Ltd (1999)
- Belsito S, Banerjee S, AIChEJ 44 (12) (1998) 2675.
- Baratti R, Vacca G, Servida A, Hydrocarbon Processing, 74 (1995) 35.
- Dankworth D. C, Sundaresan S, AIChEJ, 35 (1989) 1282.
- Fan J Y, Nikolau M, White R E, AIChEJ 39 (1993) 82.
- Haghshenas M Fard, M Zivdar, R Rahimi and Nasr M Esfahany, World Applied Sciences Journal, 7 (2009) 1439.
- Hoskins J C, Kaliyur K M, Himmelblau D M, AIChEJ 37 (1991) 137.
- Jagadeesh Babu P E, Arunagiri A, Reghupathi I. , and Murugesan T, Chem. Biochem. Eng, 21 (2007) 121.
- Pandharipande S L, An Introduction to Artificial Neural Networks, Nagpur: Denett Publications (2008).
- Pandharipande S L, Badhe Y P, Chem Eng World, 38 (2003) 70.
- Pandharipande S L, Badhe Y P, Chem Eng World, 38 (2003) 82.
- Pandharipande S L, Badhe Y P, IChE 45 (2003) 256.
- Pandharipande S L, Agarwal R S, Gogte B B, Badhe Y P, Chem Eng World 38 (2003) 78.
- Pandharipande S L, Mandavgane S A, Indian J Chem Technol 11 (2004) 820.
- Pandharipande S L, Bhaise A, Poharkar A, Chem. Eng world 39 (2004) 50.
- Pandharipande S L, Badhe Y P, J Inst Eng 84(2004) 65
- Pandharipande S L, Badhe Y P, 2004. elite-ANN©, ROC No SW-1471.
- Pandharipande S L, Shah A M, Heena T, International Journal of Computer Applications 41(2012) 23.
- Pandharipande S L, Khonde R D, International Journal of Computer Applications 41(2012) 1.
- Rao V G, Ananth M S, Varma Y B G, AlChE Journal 29 (1983) 467.
- Romulus D, Gheorghe S, Grigore B and Valentin P, Revue Roumaine de Chimie 51 (2006) 219.
- Rumelhart D. E, McClleland, Back Propagation Training Algorithm Processing, M. I. T Press, Cambridge Massachusetts (1986).
- Saez A. E, Carbonell R. G, AlChE Journal 31(1985) 52.
- Speechia V & Baldi G, Chemical Engineering Science 32(1977) 515.
- Shijie Liu, Chemical Engineering Science, 56 (2001) 5945.
- Stemmet C P, Jongmans J N, Van der Schaaf J, Kuster B F M, Schouten J C. , Chemical Engineering Science, 60 (2005) 6422.
- Watanabe K, Abe M, Kubota M, Himmelblau D M, AIChEJ 35 (1989) 137.
- Zamankhan P, Malinen P, Lepomaki H, AIChEJ 43(1997) 1684.
- Packed column by Katmar software 2011 trial version.
Index Terms
Keywords