Investigation of optimum H2 production from date palm waste leaves using different modeling tools |
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| Affiliation: | 1. School of Environmental Science and Engineering / Tianjin Key Lab of Biomass-wastes Utilization, Tianjin University, Tianjin 300072, China;2. Department of Civil Engineering, Pakistan Institute of Engineering Technology, Multan Pakistan;3. School of Engineering, The University of Toledo, Ohio, USA;4. School of Engineering RMIT University Melbourne Victoria 3000, Australia;5. School of Chemical Engineering of Technology, Tianjin University, Tianjin 300350, China;1. School of Chemical Science and Technology, Yunnan University, Kunming, 650091, China;2. Yunnan Province Key Lab of Wood Adhesives and Glued Products & Key Lab for Forest Resources Conservation and Utilization in the Southwest Mountains, Southwest Forestry University, Kunming 650224, China;3. State Key Laboratory for Conservation and Utilization of Bio-resources in Yunnan, Yunnan University, Kunming, 650091, China;1. Key Laboratory of Eco-chemical Engineering, Ministry of Education, International Science and Technology Cooperation Base of Eco-chemical Engineering and Green Manufacturing. Qingdao University of Science and Technology, Qingdao, 266042, China;2. College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, 266042, China;3. College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao, 266042, PR China;4. Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Materials Science and Technology, China University of Geosciences, Beijing, 100083, PR China;1. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Centro Atómico Bariloche (CNEA), Av. Bustillo 9500, R8402AGP, S.C. de Bariloche, Río Negro, Argentina;2. Department of Chemistry, University of Burgos, 09001, Burgos, Spain;3. International Research Center in Critical Raw Materials for Advanced Industrial Technologies (ICCRAM), University of Burgos, 09001 Burgos, Spain;4. Universidad Nacional de Cuyo (Instituto Balseiro)-Centro Atómico Bariloche (CNEA), Av. Bustillo 9500, R8402AGP, S.C. de Bariloche, Río Negro, Argentina;1. The Key Laboratory of Biotechnology for Medicinal Plant of Jiangsu Province, School of Life Sciences, Jiangsu Normal University, Xuzhou, China;2. School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea |
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| Abstract: | Globally, the productive utilization of biomass has paid serious attention to fulfilling the energy requirements laid out by the international standards, as to reduce related carbon footprints. Therefore, this study investigates date palm waste leaves which aims to produce environment friendly H2 gas using gasification technology. The results of 25 experimental runs exhibited that the higher H2 produced at higher temperature which was mainly supported by water-gas-shift and steam-methane reforming reactions. H2 prediction was modeled using response surface methodology (RSM) and artificial neural network (ANN). The RSM model exhibited a strong interaction with the regression coefficient (R2) and p-value of 0.89 and 0.000000, respectively. ANN data was disseminated thru K-fold contrivance with back-propagation algorithm. Hence, the training (80% data) and validation (20% data) datasets were found with R2 and root mean squared error (RSME) of 0.90 and 0.28, and 0.86 and 0.39, respectively. Kinetics of the process estimated the activation energies (Ea) using Ozawa-Flynn-Wall (OFW), Starink (STK), and Kissinger-Akahira-Sunose (KAS) models. Hence, the values of Ea and R2 at conversion degrees (α) 0.1 to 0.8 were ranged between 129.40 and 326.64 kJ/mol and 0.92 to 0.97, respectively. Optimum H2 production of 49.03 vol% (with LHV of 11.10 MJ/Nm3) was produced. This finding is thought to be a better source of energy which can be an appropriate fuel for Fischer Tropsch process for manufacturing of transportation fuels. |
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| Keywords: | Hydrogen Biomass gasification Date palm waste Artificial neural network Response surface methodology |
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