Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications
نویسندگان
چکیده
Thermogravimetric analysis (TGA) was utilised to compare the thermal stability of pure phase change material (D-mannitol) that nano-enhanced PCM (NEPCM) (i.e., containing 0.5% and 1% multiwalled carbon nanotubes (MWCNT)). Using model-free kinetics techniques, NEPCM degradation were analysed. Three different kinetic models such as Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO), Starink applied assess activation energies samples. Activation for using Ozawa, KAS, methods ranged from 71.10–77.77, 79.36–66.87, 66.53–72.52 kJ/mol, respectively. NEPCM’s (1% MWCNT) 76.59–59.11, 71.52–52.28, 72.15–53.07 kJ/mol. Models machine learning predict samples; these included linear regression, support vector random forests, gaussian process artificial neural network models. The mass loss sample functioned output parameter, while addition nanoparticles weight fraction, heating rate, temperature input parameters. Experiment-based TGA data can be accurately predicted created
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ژورنال
عنوان ژورنال: Energies
سال: 2023
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en16052187