Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications

Ravi Kumar Kottala, Bharat Kumar Chigilipalli, Srinivasnaik Mukuloth, Ragavanantham Shanmugam, Venkata Charan Kantumuchu, Sirisha Bhadrakali Ainapurapu and Muralimohan Cheepu ()
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Ravi Kumar Kottala: Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
Bharat Kumar Chigilipalli: Department of Mechanical Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam 530049, Andhra Pradesh, India
Srinivasnaik Mukuloth: Department of Mechanical Engineering, Chaitanya Deemed to be University, Warangal 506001, Telangana, India
Ragavanantham Shanmugam: School of Engineering, Math and Technology, Navajo Technical University, Crownpoint, NM 87313, USA
Venkata Charan Kantumuchu: Electrex Inc., Hutchinson, KS 67501, USA
Sirisha Bhadrakali Ainapurapu: Department of Mechanical Engineering, Aditya Engineering College (A), Surampalem 533437, Andhra Pradesh, India
Muralimohan Cheepu: Department of Materials System Engineering, Pukyong National University, Busan 48547, Republic of Korea

Energies, 2023, vol. 16, issue 5, 1-24

Abstract: 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.

Keywords: thermal degradation kinetics; multi walled carbon nanotubes; phase change materials; thermal storage; machine learning models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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