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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
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Elsevier
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/87766 |
| _version_ | 1848764937044754432 |
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| author | Yap, Tshun Li Loy, Adrian Chun Minh Chin, Bridgid 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 Serene Sow Mun, Lock |
| author_facet | Yap, Tshun Li Loy, Adrian Chun Minh Chin, Bridgid 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 Serene Sow Mun, Lock |
| author_sort | Yap, Tshun Li |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:27:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-87766 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:27:17Z |
| publishDate | 2022 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-877662024-04-22T00:58:12Z Synergistic Effects of Catalytic Co-Pyrolysis Chlorella vulgaris and Polyethylene Mixtures using Artificial Neuron Network: Thermodynamic and Empirical Kinetic Analyses Yap, Tshun Li Loy, Adrian Chun Minh Chin, Bridgid 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 Serene Sow Mun, Lock 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/87766 10.1016/j.jece.2022.107391 http://creativecommons.org/licenses/by-nc-nd/4.0/ Elsevier fulltext |
| spellingShingle | Yap, Tshun Li Loy, Adrian Chun Minh Chin, Bridgid 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 Serene Sow Mun, Lock Synergistic Effects of Catalytic Co-Pyrolysis Chlorella vulgaris and Polyethylene Mixtures using Artificial Neuron Network: Thermodynamic and Empirical Kinetic Analyses |
| title | 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_short | 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 |
| url | http://hdl.handle.net/20.500.11937/87766 |