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AuthorDaryayehsalameh, Bahador
AuthorAyari, Mohamed Arselene
AuthorTounsi, Abdelouahed
AuthorKhandakar, Amith
AuthorVaferi, Behzad
Available date2024-04-22T10:49:17Z
Publication Date2022-05-05
Publication NameAdvances in Nano Research
Identifierhttp://dx.doi.org/10.12989/anr.2022.12.5.489
CitationDaryayehsalameh, B., Ayari, M. A., Tounsi, A., Khandakar, A., & Vaferi, B. (2022). Differentiation among stability regimes of alumina-water nanofluids using smart classifiers. Adv. Nano Res., 12(5), 489-499.
ISSN2287-237X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131429262&origin=inward
URIhttp://hdl.handle.net/10576/54062
AbstractNanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables
Languageen
PublisherTechno Press
SubjectAlumina-water nanofluids
Artificial intelligent classifiers
Classification accuracy
Multilayer perceptron
Stability regime
TitleDifferentiation among stability regimes of alumina-water nanofluids using smart classifiers
TypeArticle
Pagination489-499
Issue Number5
Volume Number12
ESSN2287-2388
dc.accessType Abstract Only


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