Development of optimal Ann topology for data driven methane steam reforming using levenberg-marquardt method /
Siti Nurain Binti Hisham,
Development of optimal Ann topology for data driven methane steam reforming using levenberg-marquardt method / Siti Nurain Binti Hisham - x, 33 pages : illustrations ;
Faculty of Chemical and Process Engineering Technology
Final Year Report (Bachelor of Chemical Engineering Technology) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025
Include bibliographical reference
Methane 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.
THE0010509 (Local)
Faculty of Chemical and Process Engineering Technology--Dissertations
Universities and colleges--Dissertations
Theses--Dissertations
Development of optimal Ann topology for data driven methane steam reforming using levenberg-marquardt method / Siti Nurain Binti Hisham - x, 33 pages : illustrations ;
Faculty of Chemical and Process Engineering Technology
Final Year Report (Bachelor of Chemical Engineering Technology) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025
Include bibliographical reference
Methane 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.
THE0010509 (Local)
Faculty of Chemical and Process Engineering Technology--Dissertations
Universities and colleges--Dissertations
Theses--Dissertations