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Application of artificial neural network and response surface methodology for modelling of hydrogen production using nickel loaded zeolite
Fazureen Azaman1, Azman Azid2, Hafizan Juahir3, Mahadhir Mohamed4, Kamaruzzaman Yunus5, Mohd Ekhwan Toriman6, Ahmad Dasuki Mustafa7, Mohammad Azizi Amran8, Che Noraini Che Hasnam9, Roslan Umar10, Norsyuhada Hairoma11.
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 (GCTCD), 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.
Affiliation:
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- International Islamic University Malaysia, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
- Universiti Sultan Zainal Abidin, Malaysia
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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