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    Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses

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    Date
    2022
    Author
    Yap, Tshun Li
    Loy, Adrian Chun Minh
    Chin, Bridgid Lai Fui
    Lim, Juin Yau
    Alhamzi, Hatem
    Chai, Yee Ho
    Yiin, Chung Loong
    Cheah, Kin Wai
    Wee, Melvin Xin Jie
    Lam, Man Kee
    Jawad, Zeinab Abbas
    Yusup, Suzana
    Lock, Serene Sow Mun
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    Abstract
    The catalytic pyrolysis of Chlorella vulgaris, high-density polyethylene (Pure HDPE) and, their binary mixtures were conducted to analyse the kinetic and thermodynamic performances from 10 to 100 K/min. The kinetic parameters were computed by substituting the experimental and ANN predicted data into these iso-conversional equations and plotting linear plots. Among all the iso-conversional models, Flynn-Wall-Ozawa (FWO) model gave the best prediction for kinetic parameters with the lowest deviation error (2.28-12.76%). The bifunctional HZSM-5/LS catalysts were found out to be the best catalysts among HZSM-5 zeolite, natural limestone (LS), and bifunctional HZSM-5/LS catalyst in co-pyrolysis of binary mixture of Chlorella vulgaris and HDPE, in which the Ea of the whole system was reduced from range 144.93-225.84 kJ/mol (without catalysts) to 75.37-76.90 kJ/mol. With the aid of artificial neuron network and genetic algorithm, an empirical model with a mean absolute percentage error (MAPE) of 51.59% was developed for tri-solid state degradation system. The developed empirical model is comparable to the thermogravimetry analysis (TGA) experimental values alongside the other empirical model proposed in literature
    DOI/handle
    http://dx.doi.org/10.1016/j.jece.2022.107391
    http://hdl.handle.net/10576/53993
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    • Chemical Engineering [‎1194‎ items ]

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