Development and evaluation of mechanistic model for standard SCR, fast SCR, and NO2 SCR of NH3-SCR over Cu-ZSM-5

被引:2
|
作者
Zhang, Zhiqing [1 ]
Li, Dongmei [1 ]
Lan, Guanglin [2 ]
Yin, Zibin [3 ]
Pan, Mingzhang [4 ]
Jiang, Feng [1 ]
Li, Junming [1 ]
Tan, Dongli [1 ]
机构
[1] Guangxi Univ Sci & Technol, Sch Mech & Automot Engn, Liuzhou 545006, Peoples R China
[2] Beibu Gulf Univ, Sch Mech & Marine Engn, Qinzhou 535011, Peoples R China
[3] Jimei Univ, Sch Marine Engn, Xiamen 361021, Peoples R China
[4] Guangxi Univ, Coll Mech Engn, Nanning 530004, Peoples R China
关键词
Selective catalytic reduction; RSM; Optimization; SELECTIVE CATALYTIC-REDUCTION; CYCLE ENERGY-CONSUMPTION; CO2; EMISSIONS; NANOCATALYSTS; OPTIMIZATION; TRANSPORT; CHABAZITE; RSM;
D O I
10.1016/j.energy.2024.132544
中图分类号
O414.1 [热力学];
学科分类号
摘要
Selective catalytic reduction (SCR) systems are an effective way of treating NOx emissions to meet increasingly stringent international emission standards. However, the difference of reaction temperature and catalyst location will restrict the reaction of SCR to some extent. Therefore, considering the different reaction conditions, it is necessary to analyze the relationship between them for the study of SCR reaction. In this paper, Cu-ZSM-5 is selected as the catalyst in the SCR reaction, and the effects of different temperatures and catalyst locations on NH3 molar concentration and NOx conversion under three different SCR are investigated. Firstly, a dynamic model of NH3-SCR is established and verified by the experimental data of fast SCR, standard SCR, and NO2-SCR in steady state. Then, the RSM (Response Surface Methodology) is used to further optimize NOx conversion rate. The optimal reaction conditions under the three SCR reactions were obtained by RSM optimization. The results indicate that both reaction temperature and catalyst location exert significant effects on NOx conversion under the three SCR reactions. Finally, the performance of NH3-SCR over Cu zeolite catalyst is predicted and further evaluated to provide combinations and frameworks for improved performance.
引用
收藏
页数:13
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