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Artificial intelligence-assisted characterization and optimization of red mud-based nanofluids for high-efficiency direct solar thermal absorption

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dc.citedby7
dc.contributor.authorPraveen Kumar K.en_US
dc.contributor.authorKhedkar R.en_US
dc.contributor.authorSharma P.en_US
dc.contributor.authorElavarasan R.M.en_US
dc.contributor.authorParamasivam P.en_US
dc.contributor.authorWanatasanappan V.V.en_US
dc.contributor.authorDhanasekaran S.en_US
dc.contributor.authorid58862797200en_US
dc.contributor.authorid55212669600en_US
dc.contributor.authorid58961316700en_US
dc.contributor.authorid57212323035en_US
dc.contributor.authorid57283686300en_US
dc.contributor.authorid57217224948en_US
dc.contributor.authorid57205679715en_US
dc.date.accessioned2025-03-03T07:44:58Z
dc.date.available2025-03-03T07:44:58Z
dc.date.issued2024
dc.description.abstractThe utilization of nanofluids (NFs) holds promise for enhancing the thermal efficiency of solar thermal collectors. Among the various NF solutions, red mud (RM) NFs have gained attention due to their effective absorption of solar thermal energy. RM comprises precious metal oxides, making it a proficient medium for direct solar heat absorption. This study aimed to formulate water-based RM NFs with concentrations ranging from 0.1 to 0.75 vol%. Within the temperature range of 303?333 K, we assessed the specific heat (SH), viscosity (VST), and thermal conductivity (TC) of the NFs. To maintain stability, we employed polyvinylpyrrolidone (PVP) surfactant. The results indicated that the SH of RM NFs is lower than that of water. Additionally, as RM NF concentrations increased, there was a significant improvement in TC. The highest TC enhancement of 36.9 % is observed at 333 K for a concentration of 0.75 vol% compared to water. Based on the gathered data, unique equations were developed to estimate the properties of RM NFs within the studied range. Our findings suggest that RM NFs have the potential to effectively replace water in solar energy applications. Furthermore, we employed innovative ensemble-type machine learning (ML) techniques, namely Adaptive Boosting (AdaBoost) and random forest (RF), to address the problem. We also utilized these novel ML methods to construct metamodels for predicting the considered properties, offering accurate and efficient models for analyzing NF behavior. The incorporation of RM in solar thermal applications could contribute to resolving disposal challenges associated with this waste material, thereby aiding in its long-term management. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo104087
dc.identifier.doi10.1016/j.csite.2024.104087
dc.identifier.scopus2-s2.0-85184014689
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184014689&doi=10.1016%2fj.csite.2024.104087&partnerID=40&md5=602ca171028e5dfa33a0fb60c714f29c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36827
dc.identifier.volume54
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleCase Studies in Thermal Engineering
dc.subjectAdaptive boosting
dc.subjectForestry
dc.subjectMachine learning
dc.subjectNanofluidics
dc.subjectSolar heating
dc.subjectSolar thermal energy
dc.subjectThermal conductivity
dc.subjectDirect solar
dc.subjectHigher efficiency
dc.subjectMachine-learning
dc.subjectNanofluids
dc.subjectOptimisations
dc.subjectProperty
dc.subjectRed mud
dc.subjectSolar thermal
dc.subjectThermal absorptions
dc.subjectThermal-efficiency
dc.subjectSpecific heat
dc.titleArtificial intelligence-assisted characterization and optimization of red mud-based nanofluids for high-efficiency direct solar thermal absorptionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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