Co-combustion of sewage sludge and coffee grounds under increased O2/CO2 atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling

被引:73
|
作者
Chen, Jiacong [1 ]
Xie, Candie [1 ]
Liu, Jingyong [1 ]
He, Yao [1 ]
Xie, Wuming [1 ]
Zhang, Xiaochun [1 ]
Chang, Kenlin [1 ,3 ]
Kuo, Jiahong [1 ]
Sun, Jian [1 ]
Zheng, Li [1 ]
Sun, Shuiyu [1 ]
Buyukada, Musa [2 ]
Evrendilek, Fatih [2 ]
机构
[1] Guangdong Univ Technol, Sch Environm Sci & Engn, Inst Environm Hlth & Pollut Control, Guangzhou 510006, Guangdong, Peoples R China
[2] Abant Izzet Baysal Univ, Dept Environm Engn, TR-14052 Bolu, Turkey
[3] Natl Sun Yat Sen Univ, Inst Environm Engn, Kaohsiung 80424, Taiwan
关键词
Mixed O-2/CO2 atmosphere; Thermogravimetric analysis; Empirical modeling; Kinetics; OXY-FUEL COMBUSTION; THERMOGRAVIMETRIC DATA; THERMAL-ANALYSIS; COAL COMBUSTION; WATER HYACINTH; BIOMASS; PYROLYSIS; PREDICTION; PARAMETERS; OXIDATION;
D O I
10.1016/j.biortech.2017.11.031
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
(Co-)combustion characteristics of sewage sludge (SS), coffee grounds (CG) and their blends were quantified under increased O-2/CO2 atmosphere (21, 30, 40 and 60%) using a thermogravimetric analysis. Observed percentages of CG mass loss and its maximum were higher than those of SS. Under the same atmospheric O-2 concentration, both higher ignition and lower burnout temperatures occurred with the increased CG content. Results showed that ignition temperature and comprehensive combustion index for the blend of 60% SS-40% CG increased, whereas burnout temperature and co-combustion time decreased with the increased O-2 concentration. Artificial neural network was applied to predict mass loss percent as a function of gas mixing ratio, heating rate, and temperature, with a good agreement between the experimental and ANN-predicted values. Activation energy in response to the increased O-2 concentration was found to increase from 218.91 to 347.32 kJ.mol(-1) and from 218.34 to 340.08 kJ.mol(-1) according to the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods, respectively.
引用
收藏
页码:230 / 238
页数:9
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