Retrofit for SCR Equipment Operation Rate Improvement under Low Load Conditions on 600-MW Supercritical Boiler

doi:10.11930/j.issn.10.11930.2015.11.138

Abstract

Abstract: In this paper, the case study is conducted on the first economizer retrofit project which is for the improvement of the SCR equipment operation rate under low load conditions on a supercritical boiler. The continuous two-month operation monitoring shows that the project is feasible in ensuring the SCR operation rate under low load conditions, and furthering lowering the exhaust gas temperature at the same time. The project provides a reliable reference for upgrading the SCR denitrification equipments in other power plants.

Key words: power plant, fuel gas denitrification, selective catalytic reduction (SCR), classification of economizer

CLC Number:

TM621
TK01+8

LUO Jiangyong, LV Xinle. Retrofit for SCR Equipment Operation Rate Improvement under Low Load Conditions on 600-MW Supercritical Boiler[J]. journal1, 2015, 48(11): 138-141.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.10.11930.2015.11.138

https://www.electricpower.com.cn/EN/Y2015/V48/I11/138

References

[1] 郝吉明，马广大. 大气污染控制工程[M]. 北京：高等教育出版社，2002.
HAO Jiming, MA Guangda. Air pollution control engineering [M]. Beijing: Higher Education Press, 2002.
[2] Hans Jense-Holm, Nan-Yu Topse, 崔建华. 选择催化还原（SCR）脱硝技术在中国燃煤锅炉上的应用（上）[J]．热力发电，2007（9）：13-18.
Hans JenseHolm, NanYu Topse, CUI Jianhua. Application of SCR denitrification technology onto coal-fired boilers in China[J]. Thermal Power Generation, 2007(9): 13-18.
[3] 石磊. 燃煤锅炉SCR法烟气脱硝技术[J]. 锅炉技术，2009（2）： 76-80.
SHI Lei. Fuel gas De-NOx technology of SCR method in coal- fired boiler [J]. Boiler Technology, 2009(2): 76-80.
[4] 黄波，李庆，李前宇，等. SCR烟气脱硝技术在国华三河发电厂的应用[J]. 华北电力技术，2009（2）：29－31．
HUANG Bo, LI Qing, LI Qianyu, et al. Application of SCR fuel gas denitrification technology in Guahua Sanhe power plant[J]. North China Electric Power, 2009(2): 29-31.
[5] 刘学军. SCR脱硝技术在广州恒运电厂300 MW机组上的应用[J]. 中国电力，2006，39（3）：86-89.
LIU Xuejun. SCR De-NOx Technologies applied in 300 MW units of Hengyun Power Plant [J]. Electric Power, 2006, 39(3): 86-89.
[6] 梁川，沈越. 1 000 MW机组SCR烟气脱硝系统优化运行[J]. 中国电力，2012，45（1）：41－44．
LIANG Chuan, SHEN Yue. Optimal opration of selective catalytic reduction(SCR) fuel gas denitrification system in 1 000 unit[J]. Electric Power, 2012, 45(1): 41-44.
[7] 郭金龙. 火电机组选择性催化还原脱硝喷氨的优化控制[D]. 北京：华北电力大学，2011.
GUO Jinlong. Optimization control of ammonia injection of power plant SCR denitration[D]. Beijing: North China Electric Power University, 2011.
[8] 于文清. SCR法烟气脱硝系统的控制策略在火电厂中的应用[D]. 保定：华北电力大学，2012．
YU Wenqing. Control strategy of SCR fuel gas denitration system used in a thermal power plant[D]. Baoding: North China Electric Power University, 2012.

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