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Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data

Reduced graphene oxide (rGO) has applications in Water purification, and Energy conversion. In this study, a water-based nanofluid containing rGO was formed at specific concentrations. The nanofluid Thermo-Rheology behavior was studied at room temperature to 50 °C. Viscosity was detected at specific...

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Main Authors: Zhang, Yao, Selamat, Ali, Zhang, Yuxin, Alrabaiah, Hussam, Omar, Abdullah Hisam
Format: Article
Published: Elsevier Ltd 2022
Subjects:
Q Science (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/104538/
http://dx.doi.org/10.1016/j.seta.2022.102062
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spelling my.utm.1045382024-02-14T04:10:12Z http://eprints.utm.my/104538/ Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data Zhang, Yao Selamat, Ali Zhang, Yuxin Alrabaiah, Hussam Omar, Abdullah Hisam Q Science (General) TK Electrical engineering. Electronics Nuclear engineering Reduced graphene oxide (rGO) has applications in Water purification, and Energy conversion. In this study, a water-based nanofluid containing rGO was formed at specific concentrations. The nanofluid Thermo-Rheology behavior was studied at room temperature to 50 °C. Viscosity was detected at specific RPMs from 10 to 100. The results showed that this nanofluid has excellent thermo-rheology properties. Flat plate solar collectors could heat the fluid inside using sunlight from a wide range of varied angles. Thus, the prepared nanofluid was used as the working fluid in the solar collector tubes. The results showed that this nanofluid can be used instead of water. The aims of this study are to optimize the process and lessen the examination costs, thus, Artificial Neural Networks algorithms of Orthogonal Distance Regression (ODR), Levenberg Marquardt (LM), and Fuzzy system of Recursive Least Squares were trained. Results proved that Artificial Neural Network and Fuzzy systems should be trained to predict the data of thermal conductivity and viscosity with acceptable coefficient of determination. Elsevier Ltd 2022-08 Article PeerReviewed Zhang, Yao and Selamat, Ali and Zhang, Yuxin and Alrabaiah, Hussam and Omar, Abdullah Hisam (2022) Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data. Sustainable Energy Technologies and Assessments, 52 (NA). pp. 1-11. ISSN 2213-1388 http://dx.doi.org/10.1016/j.seta.2022.102062 DOI:10.1016/j.seta.2022.102062
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle Q Science (General)
TK Electrical engineering. Electronics Nuclear engineering
Zhang, Yao
Selamat, Ali
Zhang, Yuxin
Alrabaiah, Hussam
Omar, Abdullah Hisam
Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
description Reduced graphene oxide (rGO) has applications in Water purification, and Energy conversion. In this study, a water-based nanofluid containing rGO was formed at specific concentrations. The nanofluid Thermo-Rheology behavior was studied at room temperature to 50 °C. Viscosity was detected at specific RPMs from 10 to 100. The results showed that this nanofluid has excellent thermo-rheology properties. Flat plate solar collectors could heat the fluid inside using sunlight from a wide range of varied angles. Thus, the prepared nanofluid was used as the working fluid in the solar collector tubes. The results showed that this nanofluid can be used instead of water. The aims of this study are to optimize the process and lessen the examination costs, thus, Artificial Neural Networks algorithms of Orthogonal Distance Regression (ODR), Levenberg Marquardt (LM), and Fuzzy system of Recursive Least Squares were trained. Results proved that Artificial Neural Network and Fuzzy systems should be trained to predict the data of thermal conductivity and viscosity with acceptable coefficient of determination.
format Article
author Zhang, Yao
Selamat, Ali
Zhang, Yuxin
Alrabaiah, Hussam
Omar, Abdullah Hisam
author_facet Zhang, Yao
Selamat, Ali
Zhang, Yuxin
Alrabaiah, Hussam
Omar, Abdullah Hisam
author_sort Zhang, Yao
title Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
title_short Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
title_full Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
title_fullStr Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
title_full_unstemmed Artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
title_sort artificial neural networks/least squares fuzzy system methods to optimize the performance of a flat-plate solar collector according to the empirical data
publisher Elsevier Ltd
publishDate 2022
url http://eprints.utm.my/104538/
http://dx.doi.org/10.1016/j.seta.2022.102062
_version_ 1792147804093677568
score 13.226497

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