Research Article
Combined Artificial Neural Network Model for Estimation of Pressure Drop for Flow of CMC and Soil in Aqueous Solution
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 81 - Issue 17 |
| Published: November 2013 |
| Authors: Shekhar Pandharipande, Rachana S. Ranshoor |
10.5120/14216-2417
|
Shekhar Pandharipande, Rachana S. Ranshoor . Combined Artificial Neural Network Model for Estimation of Pressure Drop for Flow of CMC and Soil in Aqueous Solution. International Journal of Computer Applications. 81, 17 (November 2013), 20-26. DOI=10.5120/14216-2417
@article{ 10.5120/14216-2417,
author = { Shekhar Pandharipande,Rachana S. Ranshoor },
title = { Combined Artificial Neural Network Model for Estimation of Pressure Drop for Flow of CMC and Soil in Aqueous Solution },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 81 },
number = { 17 },
pages = { 20-26 },
doi = { 10.5120/14216-2417 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Shekhar Pandharipande
%A Rachana S. Ranshoor
%T Combined Artificial Neural Network Model for Estimation of Pressure Drop for Flow of CMC and Soil in Aqueous Solution%T
%J International Journal of Computer Applications
%V 81
%N 17
%P 20-26
%R 10.5120/14216-2417
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Estimation of pressure drop for flow of Non -Newtonian fluid is a common situation & conventional models fail to address it with high accuracy & are to be system specific. Present work is aimed to explore the possible use of the Artificial Neural Network in developing combined models for the estimation of pressure drop as a function of flowrate, density, & concentration of CMC & soil in water mixture in a pipeline. Experimental runs are conducted & the 81 data points generated are divided into 64 & 17 as training & test data points respectively. The RMSE values for S1 & C1 models are 0. 023 & 0. 016 respectively. Further evaluation done by calculating & comparing the percentage relative error shows that, most of the predicted values have accuracy level of around 90% & is acceptable. The present work has successfully highlighted the potential of Artificial Neural Network in modeling complex processes.
References
- R. P. Chabra "Non-Newtonian Fluids: An Introduction"
- Shankar, P. , Vyas, H. , Kalaichelvi, P. & Muthamizhi, K. 2012. Experimental Analysis of Mixing Characteristics of Carboxymethyl Cellulose Solutions in a Doube Jet Mixer.
- Pinho, F. T. & Whitelaw, J. H. 1990. Flow of Non-Newtonian Fluid in Pipe.
- Leal, A. B. , Calcada, L. A. & Scheid, C. M. 2nd Mercosur Congress On Chemical Engineering, 4th Mercosur Congress on Process Systems Engineering, Proceeding in ENPROMER.
- Diaz, D. G & Navaza, J. M. 2003. Rheology of Aqueous Solutions of food Addictives Effect of Concentration, Temperature & Blending.
- Venneker, B. C. H. , Derksen, J. J, Van Den Ayyer, & Harry, E. A. 2010. Turbulent Flow of Shear Thinning Fluid in Strried Tank
- Pinho, F. T. , Oliveira, P. J. & Miranda, J. P. , 2003. Pressure Losses in the Laminar Flow of Shear Thinning Power-Law Fluid across a Sudden Axisymmetric Expansion
- Joris, I. and Feyen, J. 2003. Modeling water flow and Seasonal soil moisture dynamics in an alluvial ground Water-fed wetland
- M. R. Mustafa, M. R, Isa, M. H. , Rezaur, R. B. 2012. Artificial Neural Networks Modeling in Water Resources Engineering: Infrastructure and Applications
- Rumelhart D E & McClleland, 1986 Back Propagation Training Algorithm Processing M. I. T Press, Cambridge Massachusett
- Pandharipande S. L. & Badhe, Y. P. , 2004 elite-Ann©, ROC No SW-1471.
- Pandharipande, S. L. & Ankit Singh, 2012 Optimizing Topology In Developing Artificial Neural Network Model for Estimation of Hydrodynamics of Packed Column.
- Pandharipande, S. L. & Ankit Singh, 2012 Estimation of Pressure Drop of Packed Column Using Artificial Neural Network.
- Pandharipande, S. L. , & Badhe, Y. P. 2003. Modeling of Artificial Neural Network for Leak Detection in Pipe Line.
- Pandharipande, S. L. Moharkar, Y. 2012 Artificial Neural Network Modeling of Equilibrium Relationship for Partially Miscible Liquid-Liquid Ternary System
- Pandharipande, S. L. Mandavgane, S. A. 2004 Modeling of Packed Bed Using Artificial Neural Network.
- Pandharipande, S. L. Shah, A. M. Tabassum, H 2012. Artificial Neural Network Modeling for Estimation of Composition of a Ternary Liquid Mixture with its Physical Properties such as Refractive Index, pH and Conductivity
Index Terms
Keywords