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AuthorYap, Tshun Li
AuthorLoy, Adrian Chun Minh
AuthorChin, Bridgid Lai Fui
AuthorLim, Juin Yau
AuthorAlhamzi, Hatem
AuthorChai, Yee Ho
AuthorYiin, Chung Loong
AuthorCheah, Kin Wai
AuthorWee, Melvin Xin Jie
AuthorLam, Man Kee
AuthorJawad, Zeinab Abbas
AuthorYusup, Suzana
AuthorLock, Serene Sow Mun
Available date2024-04-22T04:57:06Z
Publication Date2022
Publication NameJournal of Environmental Chemical Engineering
ResourceScopus
ISSN22133437
URIhttp://dx.doi.org/10.1016/j.jece.2022.107391
URIhttp://hdl.handle.net/10576/53993
AbstractThe 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
SponsorThe authors would like to express their sincere gratitude to the Curtin University Malaysia and the Centre of Biofuel and Biochemical (CBBR) of Universiti Teknologi PETRONAS (UTP) for the technical support. Also, Loy A. C. M. would like to thank the Australian Government , Australia for providing financial (Research Training Program) support to this project.
Languageen
PublisherElsevier
SubjectArtificial neural network
Catalytic pyrolysis
Empirical modelling
Genetic algorithm
Kinetic analysis
Microalgae Chlorella vulgaris
TitleSynergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses
TypeArticle
Issue Number3
Volume Number10
dc.accessType Abstract Only


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