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dc.contributor.authorPraveen Kumar, Kumar
dc.contributor.authorKhedkar, Rohit
dc.contributor.authorSharma, Prabhakar
dc.contributor.authorElavarasan, Rajvikram Madurai
dc.contributor.authorParamasivam, Prabhu
dc.contributor.authorWanatasanappan, V. Vicki
dc.contributor.authorDhanasekaran, Sesathiri
dc.date.accessioned2024-09-03T07:14:04Z
dc.date.available2024-09-03T07:14:04Z
dc.date.issued2024-01-30
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 waterbased 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.en_US
dc.identifier.citationPraveen Kumar, Khedkar, Sharma, Elavarasan, Paramasivam, Wanatasanappan, Dhanasekaran. Artificial intelligence-assisted characterization and optimization of red mud-based nanofluids for high-efficiency direct solar thermal absorption. Case Studies in Thermal Engineering. 2024;54en_US
dc.identifier.cristinIDFRIDAID 2249116
dc.identifier.doi10.1016/j.csite.2024.104087
dc.identifier.issn2214-157X
dc.identifier.urihttps://hdl.handle.net/10037/34500
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalCase Studies in Thermal Engineering
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleArtificial intelligence-assisted characterization and optimization of red mud-based nanofluids for high-efficiency direct solar thermal absorptionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)