Thermogravimetric analysis (TGA) was utilised to compare the thermal stability of pure phase change material (D-mannitol) to that of nano-enhanced PCM (NEPCM) (i.e., PCM containing 0.5% and 1% multiwalled carbon nanotubes (MWCNT)). Using model-free kinetics techniques, the kinetics of pure PCM and NEPCM degradation were analysed. Three different kinetic models such as Kissinger-Akahira-Sunose (KAS), the Flynn-Wall-Ozawa (FWO), and the Starink were applied to assess the activation energies of the pure and nano-enhanced PCM samples. Activation energies for pure PCM using the Ozawa, KAS, and Starink methods ranged from 71.10–77.77, 79.36–66.87, and 66.53–72.52 kJ/mol, respectively. NEPCM’s (1% MWCNT) activation energies ranged from 76.59–59.11, 71.52–52.28, and 72.15–53.07 kJ/mol. Models of machine learning were utilised to predict the degradation of NEPCM samples; these included linear regression, support vector regression, random forests, gaussian process regression, and artificial neural network models. The mass loss of the sample functioned as the output parameter, while the addition of nanoparticles weight fraction, the heating rate, and the temperature functioned as the input parameters. Experiment-based TGA data can be accurately predicted using the created machine learning models.
Summary
Using a thermogravimetric analyzer, the thermal stability of pure eutectic phase change material (PEPCM) (LiNO3 + NaCl) and composite eutectic PCM (CEPCM) mixture (ie, PCM containing 9% expanded graphite [EG]) was examined. PEPCM and CEPCM degradation kinetics were studied using model free kinetics methods. The activation energy of both PCM samples was evaluated using the Kissinger‐Akahira‐Sunose (KAS), Flynn‐Wall‐Ozawa (FWO), Starink, Friedman and Vyazovkin kinetic models. The calculated activation energies for Vyazovkin, Frideman, Ozawa, KAS and Starink techniques for PEPCM were 80.62‐149.2, 108.1‐180.18, 83.74‐136.17, 73.55‐127.02 and 74.64‐149.9 kJ/mol, respectively. Likewise, the activation energy of CEPCM vary between 59.4‐161.41, 83.97‐188.69, 57.1‐147.32, 54.19‐137.43 and 55‐160.65 kJ/mol. Hybrid neural networks such as ANN‐PSO and ANFIS were used to model the degradation of PCM samples. The type of PCM, the heating rate, and the temperature were applied as input parameters, while the sample's mass loss was utilized as an output parameter. The created hybrid models are capable of effectively predicting experimental TGA data.
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