The synergistic role of sludge conditioner FeCl3/Rice husk on co-combustion with coal gangue: Thermaldynamic behavior, gases pollutants control and bottom ash stabilization

文献类型： 外文期刊

第一作者： Li, Jixin

作者： Li, Jixin;He, Yiyang;Wang, Xin;Cao, Xiao;Yang, Haiyan;Chen, Lu;Xu, Suyun;Wen, Haifeng;Gu, Lin;Zhang, Hanlin

作者机构：

关键词： Coal gangue; Dewatered sludge; Co-combustion; TG-FTIR; Kinetics

期刊名称：JOURNAL OF THE ENERGY INSTITUTE （ 影响因子：6.2； 五年影响因子：5.7 ）

ISSN： 1743-9671

年卷期： 2025 年 118 卷

页码：

收录情况： SCI

摘要： Coal processing invariably generates substantial quantities of low calorific value waste, specifically coal gangue (CG), which can be advantageously utilized by combusting to yield valuable electrical energy. However, CG incur poor ignition and flame instability, and consequently is not suited to separate combustion. The co-combustion with sewage sludge (SS) has demonstrated positive impacts on energy recovery, whereas the SS dewatering may significantly influence the co-combustion behavior. Therefore, this study systematically investigated the impact of two typical sludge conditioner, namely FeCl3 & sdot;6H2O and rice husk (RH), which functions as flocculant and skeletal builder, on their synergistic role on co-combustion with CG. The thermal dynamic combustion behavior, pollutant emissions, slag tendency and bottom ash stability and toxicity were systematically studied. A robust positive synergism is observed, attributed to heat compensation and the formation of alkali metal aluminosilicates from Rh during the ignition phase. Concurrently, the temperature dependent iron oxides evolution enhances the acceleration of O2 loop, thereby promoting the char combustion. After being jointly conditioned with Rh and FeCl3, the co-combustion with CG resulted in CCi being 3.46 times higher than that of C1S3, and the average activation energy in each stage was reduced by 49.1 %. Significantly, the sludge conditioner also contributes to the reduced exhausted gases such as CO2, SO2 and NO. The Rh in SS has been found to mitigate slagging and fouling tendencies, while the retention of Cr, Cu, Ni, and Pb is greatly improved due to the stabilization of silicate minerals in CG. The Artificial Neural Network (ANN) models were established to predict the thermogravimetric experimental data of CG-SS-Rh/Fe, which aims to provide a basis for the selection of optimal operating conditions in real industrial applications.

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