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Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst

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  • Bong, Jang Tyng
  • Loy, Adrian Chun Minh
  • Chin, Bridgid Lai Fui
  • Lam, Man Kee
  • Tang, Daniel Kuok Ho
  • Lim, Huei Yeong
  • Chai, Yee Ho
  • Yusup, Suzana

Abstract

The catalytic pyrolysis of pure microalgae (M), peanut shell wastes (PS) and their binary mixtures were analysed by introducing the microalgae ash (MA) as a catalyst. The pyrolysis processes were conducted at different heating rates from 10 K/min-100 K/min to observe their thermal degradation behaviour. Additionally, Artificial Neural Network (ANN) was applied by feeding the heating rates and temperatures to predict the weight loss of the samples. The kinetic and thermodynamic parameters were also determined through three different iso-conversional kinetic models: Friedman (FR), Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO). Based on the kinetic results, FWO model achieved the lowest deviation between the activation energies (Ea) from the experimental which aligned with the ANN predicted results. The finding also shows that the activation energy (Ea) of the catalytic pyrolysis of binary mixtures was lower than the pure M and PS (Experimental: 142.56 kJ/mol; ANN forecast: 131.37 kJ/mol).

Suggested Citation

  • Bong, Jang Tyng & Loy, Adrian Chun Minh & Chin, Bridgid Lai Fui & Lam, Man Kee & Tang, Daniel Kuok Ho & Lim, Huei Yeong & Chai, Yee Ho & Yusup, Suzana, 2020. "Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313967
    DOI: 10.1016/j.energy.2020.118289
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    2. Chen, Zhiyun & Chen, Huashan & Wu, Xieyuan & Zhang, Junhui & Evrendilek, Deniz Eren & Liu, Jingyong & Liang, Guanjie & Li, Weixin, 2021. "Temperature- and heating rate-dependent pyrolysis mechanisms and emissions of Chinese medicine residues and numerical reconstruction and optimization of their non-linear dynamics," Renewable Energy, Elsevier, vol. 164(C), pages 1408-1423.
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    4. Tian, Junjian & Liu, Yu & Bi, Haobo & Li, Fengyu & Bao, Lin & Han, Kai & Zhou, Wenliang & Ni, Zhanshi & Lin, Qizhao, 2022. "Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model," Energy, Elsevier, vol. 239(PA).

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