Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses
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...
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my.unimas.ir.379422022-02-25T00:36:21Z http://ir.unimas.my/id/eprint/37942/ Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses Tshun Li, Yap Adrian Loy, Chun Minh Bridgid Chin, Lai Fui Juin Yau, Lim Hatem, Alhamzi Yee Ho, Chai Chung Loong, Yiin Kin Wai, Cheah Melvin Wee, Xin Jie Man Kee, Lam Zeinab Abbas, Jawad Suzana, Yusup Serene Lock, Sow Mun TA Engineering (General). Civil engineering (General) TP Chemical technology 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 Elsevier 2022-02-15 Article PeerReviewed text en http://ir.unimas.my/id/eprint/37942/1/catalytic1.pdf Tshun Li, Yap and Adrian Loy, Chun Minh and Bridgid Chin, Lai Fui and Juin Yau, Lim and Hatem, Alhamzi and Yee Ho, Chai and Chung Loong, Yiin and Kin Wai, Cheah and Melvin Wee, Xin Jie and Man Kee, Lam and Zeinab Abbas, Jawad and Suzana, Yusup and Serene Lock, Sow Mun (2022) Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses. Journal of Environmental Chemical Engineering, 10 (107391). pp. 1-14. ISSN 2213-3437 https://www.sciencedirect.com/science/article/pii/S2213343722002640?dgcid=coauthor https://doi.org/10.1016/j.jece.2022.107391 |
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TA Engineering (General). Civil engineering (General) TP Chemical technology Tshun Li, Yap Adrian Loy, Chun Minh Bridgid Chin, Lai Fui Juin Yau, Lim Hatem, Alhamzi Yee Ho, Chai Chung Loong, Yiin Kin Wai, Cheah Melvin Wee, Xin Jie Man Kee, Lam Zeinab Abbas, Jawad Suzana, Yusup Serene Lock, Sow Mun Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses |
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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 |
format |
Article |
author |
Tshun Li, Yap Adrian Loy, Chun Minh Bridgid Chin, Lai Fui Juin Yau, Lim Hatem, Alhamzi Yee Ho, Chai Chung Loong, Yiin Kin Wai, Cheah Melvin Wee, Xin Jie Man Kee, Lam Zeinab Abbas, Jawad Suzana, Yusup Serene Lock, Sow Mun |
author_facet |
Tshun Li, Yap Adrian Loy, Chun Minh Bridgid Chin, Lai Fui Juin Yau, Lim Hatem, Alhamzi Yee Ho, Chai Chung Loong, Yiin Kin Wai, Cheah Melvin Wee, Xin Jie Man Kee, Lam Zeinab Abbas, Jawad Suzana, Yusup Serene Lock, Sow Mun |
author_sort |
Tshun Li, Yap |
title |
Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses |
title_short |
Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses |
title_full |
Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses |
title_fullStr |
Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses |
title_full_unstemmed |
Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses |
title_sort |
synergistic effects of catalytic co-pyrolysis chlorella vulgaris and polyethylene mixtures using artificial neuron network: thermodynamic and empirical kinetic analyses |
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Elsevier |
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2022 |
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http://ir.unimas.my/id/eprint/37942/1/catalytic1.pdf http://ir.unimas.my/id/eprint/37942/ https://www.sciencedirect.com/science/article/pii/S2213343722002640?dgcid=coauthor https://doi.org/10.1016/j.jece.2022.107391 |
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1725976301655818240 |
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13.160551 |