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_aUMPSA _beng _cUMPSA _erda |
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| 090 | _aFTKKP .A36 2025 r Bc. | ||
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_aSiti Nurain Binti Hisham, _eauthor. |
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_aDevelopment of optimal Ann topology for data driven methane steam reforming using levenberg-marquardt method / _cSiti Nurain Binti Hisham |
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_aKuantan, Pahang : _bUMPSA, _c2025 |
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| 264 | 4 | _c© 2025 | |
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_ax, 33 pages : _billustrations ; |
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_2rdacontent _atext |
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_2rdamedia _aunmediated |
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_2rdacarrier _avolume |
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_2rda _atext file _bPDF |
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| 500 | _aFaculty of Chemical and Process Engineering Technology | ||
| 502 | _aFinal Year Report (Bachelor of Chemical Engineering Technology) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025 | ||
| 504 | _aInclude bibliographical reference | ||
| 520 | 3 | _aMethane steam reforming is a crucial process for hydrogen and syngas production, with potential applications in sustainable energy and industrial sectors. Accurate prediction of reforming products is essential for process efficiency, environmental impact assessment, and catalyst development. This study to development the topology of ANN model using Levenberg-Marquardt method. The approach involves developing models, selecting transfer functions, different hidden layers, number of nodes, evaluating model accuracy and analyzing implications. The goal is to identify the most suitable model for predicting methane steam reforming products, which could have potential applications in sustainable energy and industrial sectors. Using MATLAB software with Artificial Neural Networks (ANNs) with Levenberg-Marquardt can optimize methane steam reforming processes, enhance productivity and cost-effectiveness. Accurate prediction of reforming products is crucial for assessing the process's environmental impact and contributing to catalyst development, improving performance and cost-effectiveness in methane steam reforming. | |
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_aFaculty of Chemical and Process Engineering Technology _xDissertations |
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_aUniversities and colleges _xDissertations |
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_aTheses _xDissertations |
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| 856 | _uhttps://umpir.ump.edu.my/id/eprint/47224 | ||
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