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Research Paper

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

[+] Author and Article Information
Hadi Salehi, Mosayyeb Amiri

Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Morteza Esfandyari1

Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iranesfandyari_90@yahoo.com

1

Corresponding author.

J. Nanotechnol. Eng. Med 2(1), 011017 (Feb 16, 2011) (3 pages) doi:10.1115/1.4003500 History: Received December 04, 2010; Revised December 09, 2010; Published February 16, 2011; Online February 16, 2011

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

MLP neural network structure

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Figure 2

The schematic of the system

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Figure 3

Training error versus number of neurons in hidden layer

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Figure 4

Variation of the temperature with respect to time

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Figure 5

Variation of the energy gain with respect to time

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Figure 6

Comparison of the predicted neural network and measured experimental data for the Nansulate system

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Figure 7

Comparison of the predicted neural network and measured experimental data for the uninsulate system

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