School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
Bioresour Technol. 2018 Feb;250:230-238. doi: 10.1016/j.biortech.2017.11.031. Epub 2017 Nov 13.
(Co-)combustion characteristics of sewage sludge (SS), coffee grounds (CG) and their blends were quantified under increased O/CO 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 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 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 concentration was found to increase from 218.91 to 347.32 kJ·mol and from 218.34 to 340.08 kJ·mol according to the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods, respectively.
采用热重分析方法，在增加的 O/CO 气氛（21、30、40 和 60%）下定量研究了污水污泥（SS）、咖啡渣（CG）及其混合物的共燃烧特性。观察到 CG 的质量损失百分比及其最大值高于 SS。在相同的大气 O 浓度下，随着 CG 含量的增加，点火温度和综合燃烧指数均升高，而燃烧温度和共燃时间均降低。结果表明，随着 O 浓度的增加，60%SS-40%CG 混合物的着火温度和综合燃烧指数增加，而燃烧温度和共燃时间降低。人工神经网络被应用于预测质量损失百分比作为气体混合比、加热速率和温度的函数，实验值与 ANN 预测值之间具有良好的一致性。根据 Kissinger-Akahira-Sunose 和 Flynn-Wall-Ozawa 方法，发现随着 O 浓度的增加，活化能分别从 218.91 kJ·mol 和 218.34 kJ·mol 增加到 347.32 kJ·mol 和 340.08 kJ·mol。
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