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Reseach Article

Combined Artificial Neural Network Model for Estimation of Pressure Drop for Flow of CMC and Soil in Aqueous Solution

by Shekhar Pandharipande, Rachana S. Ranshoor
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 17
Year of Publication: 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 },
issue_date = { November 2013 },
volume = { 81 },
number = { 17 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number17/14216-2417/ },
doi = { 10.5120/14216-2417 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:19.510601+05:30
%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
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 17
%P 20-26
%D 2013
%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
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Soil-CMC-Water Solution Pressure Drop Estimation Non-Newtonian Fluid.