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

Author

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  • Ravi Kumar Kottala

    (Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
    Department of Mechanical Engineering, M V G R College of Engineering (A), Vizianagaram 535005, Andhra Pradesh, 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
    STARWELDS Inc., Busan 46722, Republic of Korea)

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.

Suggested Citation

  • Ravi Kumar Kottala & Bharat Kumar Chigilipalli & Srinivasnaik Mukuloth & Ragavanantham Shanmugam & Venkata Charan Kantumuchu & Sirisha Bhadrakali Ainapurapu & Muralimohan Cheepu, 2023. "Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications," Energies, MDPI, vol. 16(5), pages 1-24, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2187-:d:1079107
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    References listed on IDEAS

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