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_aTHE0010517 (Local) _qHardback |
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_aUMPSA _beng _cUMPSA _erda |
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| 090 | _aFTKKP .H39 2025 r Bc | ||
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_aMuhammad Hazman Kamil Bin Munauwir, _eauthor. |
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| 245 | 1 | 0 |
_aOptimal artificial neural network for data driven methane steam reforming model using bfgs quasi- newton / _cMuhammad Hazman Kamil Bin Munauwir |
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_aKuantan, Pahang : _bUMPSA, _c2025 |
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| 264 | 4 | _c© 2025 | |
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_axi, 37 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 (MSR) is a commonly utilized method of hydrogen synthesis, contributing significantly to the global supply. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton method is an iterative technique for solving nonlinear optimization problems. This work investigates the use of MATLAB tools to apply the BFGS Quasi-Newton approach for data-driven process design in the context of methane steam reforming. The BFGS algorithm is a second-order optimization method that uses gradients to approximate the Hessian matrix's inverse. Despite being more computationally demanding and requiring more storage than conjugate gradient approaches, it converges quickly and is suitable for large datasets. The study uses the BFGS Quasi-Newton method to optimize methane steam reforming, focusing on minimizing performance along the search direction and determining the minimum point using MATLAB for optimization problems. The BFGS Quasi-Newton method, when combined with MATLAB tools, provides a robust and efficient method for data-driven process design in methane steam reforming, potentially enhancing hydrogen production efficiency. | |
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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/47256 | ||
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_c104796 _d104802 |
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