APPLICATION OF ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY FOR MODELLING OF HYDROGEN PRODUCTION USING NICKEL LOADED ZEOLITE

Authors

  • Fazureen Azaman East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Azman Azid East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Hafizan Juahir East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Mahadhir Mohamed bDepartment of Chemical Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Kamaruzzaman Yunus Kulliyah of Science, International Islamic University Malaysia, Jalan Istana, Bandar Indra Mahkota, 25200 Kuantan, Pahang, Malaysia
  • Mohd Ekhwan Toriman East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Ahmad Dasuki Mustafa East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Mohammad Azizi Amran East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Che Noraini Che Hasnam East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Roslan Umar East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia
  • Norsyuhada Hairoma East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.4265

Keywords:

Hydrogen gas, glycerol steam reforming, Ni-HZSM-5, response surface methodology, artificial neural network

Abstract

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.

References

Gerpen, J. V. 2005. Biodiesel Processing and Production. Fuel Processing Technology 86: 1097-1107.

Shawn, P. and Conley, B. T. 2006. What is Biodiesel? BioEnergy. ID337.

Xiaohu, F. R. B. a. Y. Z. 2010. Glycerol (Byproduct of Biodiesel Production) as a Source of Fuels and Chemicals-Mini Review. The Open Fuels and Energy Science Journal. 3: 17-22.

Adhikari, S., Fernando, S. D. and Agus Haryanto. 2009. Hydrogen Production from Glycerol: An update. Energy Conversion and Management. 50: 2600-2604.

Adhikari, S., Fernando, S., To, S. D. F., Bricka, R. M., Steele, P. H. and Haryanto, A. 2008. Conversion of Glycerol to Hydrogen via a Steam Reforming Process over Nickel Catalysts. Energy Fuel. 22(2): 1220-1226.

Iriondo, A., Barrio, V. L., Cambra, J. F., Arias, P. L., Guemez, M. B., Navarro, R. M., Sanchez-Sanchez, M. C. and Fierro, J. L. G. 2009. Influence of La2O3 Modified Support and Ni and Pt Active Phases on Glycerol Steam Reforming to Produce Hydrogen. Catalysis Communications. 10: 1275-1278.

Nichele, V., Signoretto, M., Menegazzo, F., Gallo, A., Santo, V. D., Cruciani, G. and Cerrato, G. 2012. Glycerol Steam Reforming for Hydrogen Production: Design of Ni Supported Catalysts. Applied Catalysis B: Environment. 111: 225-232.

Buhari, J. 2013. Hydrogen Production from Glycerol using Nickel Loaded Zeolite Catalyst. Degree Thesis. Universiti Teknologi Malaysia.

Myers, R. H. and Montgomery, D. H. 2002. Response Surface Methodology: Process and Product Optimization Using Designed Experiment. A Wiley-Inter-science Publication.

Maran, J. P., Sivakumar, V., Sridhar, R. and Prince Immanuel, V. 2013. Development of Model for Mechanical Properties of Tapioca Starch Based Edible ï¬lms. Ind. Crop. Prod. 42: 159-168.

Maran, J. P., Sivakumar, V., Sridhar, R. and Thirgananasambandham, K. 2013. Development of Model for Barrier and Optical Properties of Tapioca Starch Based Edible ï¬lms. Carbohyd. Polym. 92: 1335-1347.

Banerjee, R. and Bhattacharyya, B. C. 1992. Optimization of Multiple Inducers Effect on Protease Biosynthesis by Rhizopus oryzae. Bioprocess Eng. 7: 225-228.

De Coninck, J., Bouquelet, S., Dumortier, V., Duyme, F. and Denantes, V. 2000. Industrial Media and Fermentation Process for Improved Growth and Protease Production by Tetrahymena Thermophile BIII. J Ind Microbiol Biotechnol. 24: 285-290.

Murthy, M. S. R. C., Swaminathan, T., Rakshit and Kosugi, Y. 2000. Statistical Optimization of Lipase Catalyzed Hydrolysis of Methyloleate by Response Surface Methodology. Bioprocess Eng. 22: 35-39.

Kusworo, T. D., Songip, A. R. and Amin, N. A. S. 2010. Optimization of Partial Oxidation of Methane for Hydrogen Production on NiO-CoO/MgO Catalyst using Design of Experiment. IJET-IJENS. 10(1): 1-8.

Azid, A., Juahir, H., Latif, M. T., Zain, S. M. and Osman, M. R. 2013. Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia. J. Environ. Prot. 4: 1-10. doi: 10.4236/jep.2013.412A001.

Mutalib, S. N. S. A., Juahir, H., Azid, A., Sharif, S. M., Latif, M. T., Aris, A. Z., Zain, S. M. and Dominick, D. 2013. Spatial and Temporal Air Quality Pattern Recognition Using Environmetric Techniques: A Case Study in Malaysia. Environ. Sci. Processes Impacts. 15: 1717-1728. doi: 10.1039/c3em00161j.

Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam, C. N. C., Aziz, N. A. A., Azaman, F., Latif, M. T., Zainuddin, S. F. M., Osman, M. R. and Yamin, M. 2014. Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: A Case Study in Malaysia. Water Air Soil Pollut. 225: 2063. doi: 10.1007/s11270-014-2063-1.

Serra, J. M., Corma, A., Argente, E., Valero, S. and Botti, V. 2003. Neural Network for Modelling of Kinetic Reaction Data Applicable to Catalyst Scale Up and Process Control and Optimisation in a Frame of Combinatorial Catalysis. Applied Catalysis A: General. 254: 133-145.

Nagata, Y. and Chu, K.H. 2003. Optimization of a Fermentation Medium Using Neural Networks and Genetic Algorithms. Biotechnol. Lett. 25: 183-1842.

Desai, K. M., Survase, S. A., Saudagar, P. S., Lele, S. S., and Singhal, R. S. 2008. Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in Fermentation Media Optimization: Case study of Fermentative Production of scleroglucan. Biochemical Engineering Journal. 41: 266-273. doi:10.1016/j.bej.2008.05.009.

Singh, A., Tatewar, D., Shastri, P. N., and Pandharipande, S. L. 2008. Application of ANN for Prediction of Cellulase and Xylanase Production by Trichoderma Reesei Under SSF Condition. Indian Journal of Chemical Technology. 15: 53-58.

Ahmed, G. E.-D. and Daniel, W. S. 2001. A Neural Network Model to Predict the Wastewater Inflow Incorporating Rainfall Events. Water Research. 36: 1115-1126.

Guan, X. and Yao, H. 2008. Optimization of Viscozyme L-Assisted Extraction of Oat Bran Protein Using Response Surface Methodology. Food Chemistry. 106: 345-351.

Demuth, H., Beale, M. and Hagan, M. 2009. Neural Network Toolbox™ 6 User’s Guide. The MathWorks Inc.

Haaland, P. D. 1989. Experimental Design in Biotechnology. New York: Marcel Dekker Inc.

Yun, H. B. A. H. 2013. Hydrogen Production from Glycerol Using Copper and Nickel Loaded Zeolite Based Catalyst. Master Thesis. Universiti Teknologi Malaysia.

Slinn, M., Kendall, K., Mallon, C. and Andrews, J. 2008. Steam Reforming of Biodiesel By-Product to make Renewable Hydrogen. Bioresource Technology. 99: 5851-5858.

Daliakopoulos, I. N., Coulibaly, P. and Tsanis, I. K. 2005. Groundwater Level Forecasting Using Artificial Neural Networks. Journal of Hydrology. 309: 229-240.

Geyikci, F., Kılıc, E., Coruh, S. and Elevli, S. 2012. Modelling of Lead Adsorption from Industrial Sludge Leachate on Red Mud by Using RSM and ANN. Chemical Engineering Journal. 183: 53-59.

Saidina Amin, N. A., Mohd Yusof, K., and Isha, R. 2005. Carbon Dioxide Reforming of Methane to Syngas: Modeling Using Response Surface Methodology and Artificial Neural Network. Jurnal Teknologi. 43: 15-30.

Mourabet, M., Rhilassi, A. El, Bennani-Ziatni, M., and Taitai, A. 2014. Comparative Study of Artificial Neural Network and Response Surface Methodology for Modelling and Optimization the Adsorption Capacity of Fluoride onto Apatitic Tricalcium Phosphate. Universal Journal of Applied Mathematics. 2(2): 84-91. doi:10.13189/ujam.2014.020202.

Downloads

Published

2015-10-21

Issue

Vol. 77 No. 1: November 2015

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

APPLICATION OF ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY FOR MODELLING OF HYDROGEN PRODUCTION USING NICKEL LOADED ZEOLITE. (2015). Jurnal Teknologi (Sciences & Engineering), 77(1). https://doi.org/10.11113/jt.v77.4265