Artificial Neural Network Modeling of Hydrogen-rich Syngas Production from Methane Dry Reforming over Novel Ni/CaFe2O4 Catalysts

• Main Authors: Hossain, M. Anwar, Ayodele, Bamidele V., Cheng, C. K., Khan, Maksudur R.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2016
• Subjects: TP Chemical technology
• Online Access: http://umpir.ump.edu.my/id/eprint/13179/1/Artificial%20Neural%20Network%20Modeling%20Of%20Hydrogen-Rich%20Syngas%20Production%20From%20Methane%20Dry%20Reforming%20Over%20Novel%20Ni-CaFe2O4catalysts.pdf; http://umpir.ump.edu.my/id/eprint/13179/; http://dx.doi.org/10.1016/j.ijhydene.2016.04.034

In this study, the application of artificial neural networks (ANN) for the modeling of hydrogen-rich syngas produced from methane dry reforming over Ni/CaFe2O4 catalysts was investigated. Multi-layer perceptron (MLP) and radial basis function (RBF) neural network architectures were employed for the...