Thermophysical properties prediction of carbon-based nano-enhanced phase change material's using various machine learning methods

• Published in: Journal of the Taiwan Institute of Chemical Engineers Vol. 148; p. 104662
• Main Authors: Gao, Yuguo, Shigidi, Ihab M.T.A., Ali, Masood Ashraf, Homod, Raad Z., Safaei, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2023
• Subjects: ARD regression; Bayesian; K-nearest neighbors; LASSO regression; Myristic acid; Nano-enhanced phase change material; LASSO regression; K-nearest neighbors; ARD regression; Myristic acid; Nano-enhanced phase change material; Bayesian
• ISSN: 1876-1070; 1876-1089
• DOI: 10.1016/j.jtice.2022.104662

•The thermophysical properties of a MA containing MWCNT, GNP, and NG nanoparticles were predicted using various ML methods.•Thermal conductivity and latent heat of 100 thermal cycles measured for 1, 2, and 3 wt.% of nanoparticles PCM were predicted.•Supervised algorithms like kKNN, ARD, and LASSO used for regression, creating formulas and making models.•The LASSO regression is used to make a relationship between melting- and freezing-states, and the R2= 0.999 was obtained for all materials.
In this modeling project, employing various machine learning methods, the thermophysical properties of phase change material (PCM) containing three nanoparticles were predicted. PCM with Multi-Walled Carbon Nanotube (MWCNT), Graphene Nanoplatelets (GNP), and nano-graphite (NG) were considered as nano-enhanced PCM, and their experimental data were extracted from the literature.
The data consisted of thermal conductivity and latent heat of 100 thermal cycles measured for 1, 2, and 3 wt.% of nanoparticles in the myristic acid (MA) as PCM. This research uses supervised algorithms such as k-nearest neighbors (KNN), Automatic relevance determination (ARD), and least absolute shrinkage and selection operator (LASSO) for regression, creating formulas and making models.
This research using the ARD regression to find the relationship between solid-state and liquid-state for three materials (GNPs/MA, MWCNTs/MA, and NG/MA) with the R-Squared value of 0.999 for all materials. The MSE of the ARD algorithms for the materials respectively is 1 × 10−9, 9 × 10−10, and 6 × 10−10. Using the MA, which is the primary material, creates the polynomial regression for GNPs/MA, MWCNTs/MA, and NG/MA, and the R-Squared values are 0.981, 0.984, and 0.981 and the MSE, respectively is 0.000159351, 0.000016945, and 0.000022425. The KNN algorithm is used to make the model for this subject, and the R-Squared values are 0.985, 0.988, and 0.988. The LASSO regression is used to make the linear regression for the relationship between melting-state and freezing-state, and the R-Squared value is 0.999 for all materials. The MSE of the LASSO method for these materials of this part respectively is 0.000176203, 0.000000545, and 0.000005035.