Please use this identifier to cite or link to this item: http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/980
Title: On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques
Authors: Jamei, Mehdi
Ahmadianfara, Iman
Olumegbon, Ismail A.
Asadi, Amin
Karbasi, Masoud
Said, Zafar
Sharifpur, Mohsen
Meyer, Josua P.
Keywords: Nanofluids
Specific heat capacity
Metal oxide
Energy storage
Kernel extreme learning machine
Multivariate adaptive regression spline
Issue Date: 2021
Publisher: International Communications in Heat and Mass Transfer
Citation: Jamei, M. ; Ahmadianfar, I. ; Olumegbon, I.S.; Asadi,A. ; Karbasi,M. ; Said,Z. ; Sharifpur,M. ; Meyer, J.P. (2021) On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques. International Communications in Heat and Mass Transfer, 123, https://doi.org/10.1016/j.icheatmasstransfer.2021.105217.
Abstract: The main aim of the present study is to investigate the capabilities of four robust machine learning method - the Kernel Extreme Learning Machine (KELM), Adaptive Regression Spline (MARS), M5 Model Tree (M5Tree), and Gene Expression Programming (GEP) model in predicting specific heat capacity (SHC) of metal oxide-based nanofluids implemented in solar energy application. Sets of 1180 data of different metal oxide-based nanofluids containing Al2O3, ZnO, TiO2, SiO2, MgO, and CuO dispersed in various base fluids were collected from reliable literature to provide the predictive model of SHC of nanofluids. The volume fraction, temperature, SHC of the base fluid, and mean diameter of nanoparticles were used as an input variable to predict nanofluids' SHC as the output variable. The artificial intelligence (AI) models were validated using several statistical performance criteria, graphical devices, and conventional models. The results obtained from all datasets demonstrated that the KELM model significantly outperformed the MARS, M5Tree, and GEP model in predicting the SHC of nanofluid. Moreover, the sensitivity analysis showed that the mean diameter of the nanoparticle and SHC of the base fluid have the most considerable impact on estimating the SHC of metal oxide-based nanofluids.
Description: Staff Publication
URI: https://doi.org/10.1016/j.icheatmasstransfer.2021.105217
http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/980
Appears in Collections:Research Articles

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