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Investigation of optimum H2 production from date palm waste leaves using different modeling tools
被引:2
|作者:
Jamro, Imtiaz Ali
[1
,2
]
Kumar, Akash
[1
]
Khoso, Salim
[3
]
Ahmad, Muhammad
[2
]
Baloch, Humair Ahmed
[4
]
Shah, Syyed Adnan Raheel
[2
]
Kumari, Lata
[5
]
Wenga, Terrence
[1
]
Nadeem, Mehwish
[1
]
Laghari, Azhar Ali
[1
]
Chen, Guanyi
[1
]
Ma, Wenchao
[1
]
机构:
[1] Tianjin Univ, Sch Environm Sci & Engn, Tianjin Key Lab Biomass wastes Utilizat, Tianjin 300072, Peoples R China
[2] Pakistan Inst Engn Technol, Dept Civil Engn, Multan, Pakistan
[3] Univ Toledo, Sch Engn, Toledo, OH USA
[4] RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia
[5] Tianjin Univ, Sch Chem Engn Technol, Tianjin 300350, Peoples R China
关键词:
Hydrogen;
Biomass gasification;
Date palm waste;
Artificial neural network;
Response surface methodology;
MUNICIPAL SOLID-WASTE;
FLUIDIZED-BED GASIFICATION;
RICH SYNGAS PRODUCTION;
HYDROGEN-PRODUCTION;
STEAM GASIFICATION;
CO-GASIFICATION;
CHLORELLA-VULGARIS;
DOWNDRAFT GASIFIER;
CHAR GASIFICATION;
BIOMASS WASTES;
D O I:
10.1016/j.ijhydene.2023.03.053
中图分类号:
O64 [物理化学(理论化学)、化学物理学];
学科分类号:
070304 ;
081704 ;
摘要:
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 acti- vation 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 (a) 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.& COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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页码:21636 / 21653
页数:18
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