Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica

Graphene oxide/silica composite�s rheological behavior was studied in this investigation. This composite was made to reduce the cost of industrial usages. The volume fractions investigated from 0.1 to 1.0 (GO 30�SiO2 70), the shear rates investigated from 12.23 to 122.3 s�1, and the temperatu...

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Main Authors: Ahmad, M.N., Mahmood, A.K., Hashim, K.F., Mustakim, F.B., Selamat, A., Bajuri, M.Y., Arshad, N.I.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.30357 /
Published: Springer Science and Business Media B.V. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359687&doi=10.1007%2fs10973-021-10687-5&partnerID=40&md5=17fb7e95ca1161c71b8eacbb6ca72b8c
http://eprints.utp.edu.my/30357/
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spelling utp-eprints.303572022-03-25T06:44:16Z Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica Ahmad, M.N. Mahmood, A.K. Hashim, K.F. Mustakim, F.B. Selamat, A. Bajuri, M.Y. Arshad, N.I. Graphene oxide/silica composite�s rheological behavior was studied in this investigation. This composite was made to reduce the cost of industrial usages. The volume fractions investigated from 0.1 to 1.0 (GO 30�SiO2 70), the shear rates investigated from 12.23 to 122.3 s�1, and the temperatures investigated from 25 to 50 °C. To study the characterization of each solid and the composite, the XRD and the FESEM tests were done. The results of the viscosity investigation revealed the non-Newtonian behavior. After that, a numerical study was done to present a correlation and train an artificial neural network model. These numerical studies were done for both 12.23 and 122.3 s�1 shear rates. The novel equation tolerances were 1.932 and 1.338 for 12.23 and 122.3 s�1 shear rates, while for the artificial neural network model, the tolerances were 1.46196 and 1.25386 for 12.23 and 122.3 s�1 shear rates. This means, after the model was trained, the deviation decreased around �0.46999 and �0.08467 for 12.23 and 122.3 s�1 shear rates. This nanofluid can be employed in industrial systems. © 2021, Akadémiai Kiadó, Budapest, Hungary. Springer Science and Business Media B.V. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359687&doi=10.1007%2fs10973-021-10687-5&partnerID=40&md5=17fb7e95ca1161c71b8eacbb6ca72b8c Ahmad, M.N. and Mahmood, A.K. and Hashim, K.F. and Mustakim, F.B. and Selamat, A. and Bajuri, M.Y. and Arshad, N.I. (2021) Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica. Journal of Thermal Analysis and Calorimetry, 145 (4). pp. 2209-2224. http://eprints.utp.edu.my/30357/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Graphene oxide/silica composite�s rheological behavior was studied in this investigation. This composite was made to reduce the cost of industrial usages. The volume fractions investigated from 0.1 to 1.0 (GO 30�SiO2 70), the shear rates investigated from 12.23 to 122.3 s�1, and the temperatures investigated from 25 to 50 °C. To study the characterization of each solid and the composite, the XRD and the FESEM tests were done. The results of the viscosity investigation revealed the non-Newtonian behavior. After that, a numerical study was done to present a correlation and train an artificial neural network model. These numerical studies were done for both 12.23 and 122.3 s�1 shear rates. The novel equation tolerances were 1.932 and 1.338 for 12.23 and 122.3 s�1 shear rates, while for the artificial neural network model, the tolerances were 1.46196 and 1.25386 for 12.23 and 122.3 s�1 shear rates. This means, after the model was trained, the deviation decreased around �0.46999 and �0.08467 for 12.23 and 122.3 s�1 shear rates. This nanofluid can be employed in industrial systems. © 2021, Akadémiai Kiadó, Budapest, Hungary.
format Article
author Ahmad, M.N.
Mahmood, A.K.
Hashim, K.F.
Mustakim, F.B.
Selamat, A.
Bajuri, M.Y.
Arshad, N.I.
spellingShingle Ahmad, M.N.
Mahmood, A.K.
Hashim, K.F.
Mustakim, F.B.
Selamat, A.
Bajuri, M.Y.
Arshad, N.I.
Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
author_sort Ahmad, M.N.
title Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
title_short Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
title_full Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
title_fullStr Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
title_full_unstemmed Artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
title_sort artificial intelligence model and correlation for characterization and viscosity measurements of mono & hybrid nanofluids concerned graphene oxide/silica
publisher Springer Science and Business Media B.V.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359687&doi=10.1007%2fs10973-021-10687-5&partnerID=40&md5=17fb7e95ca1161c71b8eacbb6ca72b8c
http://eprints.utp.edu.my/30357/
_version_ 1741197392941678592
score 11.62408

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