Application of artificial neural network and response surface methodology for modelling of hydrogen production using nickel loaded zeolite

• Main Authors: Azaman, Fazureen, Azid, Azman, Juahir, Hafizan, Mohamed, Mahadhir, Yunus, Kamaruzzaman, Toriman, Mohd Ekhwan, Mustafa, Ahmad Dasuki, Amran, Mohammad Azizi, Che Hasnam, Che Noraini, Umar, Roslan, Hairoma, Norsyuhada
• Format: Article
• Language: English
• Published: Universiti Teknologi Malaysia 2015
• Subjects: QD Chemistry
• Online Access: http://irep.iium.edu.my/45673/1/Application_of_artificial_neutral.pdf; http://irep.iium.edu.my/45673/; http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/4265

Hydrogen gas production via glycerol steam reforming using nickel (Ni) loaded zeolite (HZSM-5) catalyst was focused on this research. 15 wt % Ni(HZSM-5) catalyst loading has been investigated based on the parameter of different range of catalyst weight (0.3-0.5g) and glycerol flow rate (0.2-0.4mL/min) at 600 ºC and atmospheric pressure. The products were analyzed by using gas-chromatography with thermal conductivity detector (GC-TCD), where it used to identify the yield of hydrogen. The data of the experiment were analyzed by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in order to predict the production of hydrogen. The results show that the condition for maximum hydrogen yield was obtained at 0.4 ml/min of glycerol flow rate and 0.3 g of catalyst weight resulting in 88.35 % hydrogen yield. 100 % glycerol conversion was achieved at 0.4 of glycerol flow rates and 0.3 g catalyst weight. After predicting the model using RSM and ANN, both models provided good quality predictions. The ANN showed a clear superiority with R2 was almost to 1 compared to the RSM model.