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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
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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.

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Index Terms

Computer Science
Information Sciences

Keywords

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

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