Fusion Measurement Method for NOx Concentration of Flue Gas Based on Kalman Filter

doi:10.3901/JME.2017.24.063

Abstract

Abstract: The accurate and rapid measurement of NO_x concentration is critical to reduce the NO_x emission of flue gas and to increase the efficiency of de-NO_x system in coal-fired power plant. The measurement delay is one of the key problems for measurement of NO_x concentration in continuous emission monitoring systems (CEMS). Furthermore, CEMS is unable to make NO_x concentration measurement during the purging process of the sampling tube from. Although soft sensor is an attractive delay-free and economical measurement approach for monitoring NO_x concentration, its measurement accuracy is low. A fusion measurement method is proposed for the NO_x concentration measurement of flue gas based on Kalman filter and data fusion techniques. Numerical simulations are carried out with real operation historical data in a coal-fired power plant to evaluate the performance of the proposed method. Results demonstrated that the fusion measurement method based on Kalman filter and data fusion techniques is capable of improving response time and measurement accuracy of NO_x concentration.

Key words: continuous emission monitoring systems, data fusion, Kalman filtering, NO_x, soft sensor

CLC Number:

TK314

SHI Raoqiao, LI Jian, ZHANG Biao, XU Chuanlong, WANG Shimin. Fusion Measurement Method for NO_x Concentration of Flue Gas Based on Kalman Filter[J]. Journal of Mechanical Engineering, 2017, 53(24): 63-69.

References

[1] 张建宇, 潘荔, 杨帆, 等. 中国燃煤电厂大气污染物控制现状分析[J]. 环境工程技术学报, 2011, 1(5):185-196. ZHANG Jianyu, PAN Li, YANG Fan, et al. Study on Current status of air pollution control for coal -fired power plants in China[J]. Journal of Environmental Engineering Technology, 2011, 1(5):185-196.
[2] 张楚莹, 王书肖, 刑佳, 等. 中国能源相关的氮氧化物排放现状与发展趋势分析[J]. 环境科学学报, 2008, 28(12):2470-2479. ZHANG Chuying, WANG Shuxiao, XING Jia, et al. Current status and future projections of NO_x emissions from energy related industries in China[J]. Acta Scientiae Circumstantiae, 2008, 28(12):2470-2479.
[3] SKALSKA K, MILLER J S, LEDAKOWICZ S. Trends in NO_x abatement:A review[J]. Science of the Total Environment, 2010, 408(19):3976-89.
[4] 遆曙光, 陈智超, 蒋炳坤, 等. 多次分级中心给粉旋流煤粉高效低氮氧化物燃烧技术[J]. 机械工程学报, 2015, 51(12):146-152. TI Shuguang, CHEN Zhichao, JIANG Bingkun, et al. High efficient and low NO_x centrally fuel rich swirl burner with multi-stage combustion technology[J]. Chinese Journal of Mechanical Engineering, 2015, 51(12):146-152.
[5] MA Z, DENG J, LI Z, et al. Characteristics of NO_x emission from Chinese coal-fired power plants equipped with new technologies[J]. Atmospheric Environment, 2016, 131:164-170.
[6] 张弛. 烟气连续监测系统关键技术的研究[D]. 天津:天津大学, 2012. ZHANG Chi. Key techniques research for continuous emission monitoring system[D]. Tianjin:Tianjin University, 2012.
[7] 常英杰, 陆宪忠, 王世龙, 等. 基于偏最小二乘回归的发动机排气分析仪线性化研究[J]. 机械工程学报, 2011, 47(10):76-81. CHANG Yingjie, LU Xianzhong, WANG Shilong, et al. Study on the linearization of analyzer for engine exhaust based on partial least squares[J]. Chinese Journal of Mechanical Engineering, 2011, 47(10):76-81.
[8] 顾燕萍, 赵文杰, 吴占松. 基于最小二乘支持向量机的电站锅炉优化[J]. 中国电机工程学报, 2010, 30(17):91-97. GU Yanping, ZHAO Wenjie, WU Zhansong. Combustion optimization for utility boiler based on least square-support vector machine[J]. Proceedings of the CSEE, 2010, 30(17):91-97.
[9] LV Y, LIU J, YANG T, et al. A novel least squares support vector machine ensemble model for NO x, emission prediction of a coal-fired boiler[J]. Energy, 2013, 55(1):319-329.
[10] 潘泉, 王增福, 梁彦, 等. 信息融合理论的基本方法与进展(Ⅱ)[J]. 控制理论与应用, 2012, 29(10):1233-1244. PAN Quan, WANG zengfu, LIANG Yan, et al. Basic methods and progress of information fusion (Ⅱ)[J]. Control Theory & Applications, 2012, 29(10):1233-1244.
[11] KHALEGHI B, KHAMIS A, KARRAY F O, et al. Multisensor data fusion:A review of the state-of-the-art[J]. Information Fusion, 2013, 14(1):28-44.
[12] 宗长富, 潘钊, 胡丹, 等. 基于扩展卡尔曼滤波的信息融合技术在车辆状态估计中的应用[J]. 机械工程学报, 2009, 45(10):272-277. ZONG Changfu, PAN Zhao, HU dan, et al. Information fusion algorithm for vehicle state estimation based on extended kalman filtering[J]. Journal of Mechanical Engineering, 2009, 45(10):272-277.
[13] 黄漫国, 樊尚春, 郑德智, 等. 多传感器数据融合技术研究进展[J]. 传感器与微系统, 2010, 29(3):5-8. HUANG Manguo, FAN Shangchun, ZHEN Dezhi, et al. Research progress of multi-sensor data fusion technology[J]. Transducer and Microsystem Technologies, 2010, 29(3):5-8.
[14] 李海艳, 李维嘉, 王运保. 基于卡尔曼滤波的多传感器测量数据融合[J].武汉大学学报, 2011, 44(4):521-525. LI Haiyan, LI Weijia, WANG Yunbao. Multisensor measured data fusion based on Kalman filtering[J]. Journal of Wuhan University, 2011, 44(4):521-525.
[15] GAN Q, HARRIS C J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion[J]. Aerospace & Electronic Systems IEEE Transactions on, 2001, 37(1):273-280.
[16] ESCAMILLA-AMBROSIO P J, MORT N. Multi-sensor data fusion architecture based on adaptive Kalman filters and fuzzy logic performance assessment[C]//International Conference on Information Fusion. Piscataway:IEEE, 2002, 2:1542-1549.
[17] KALMAN R E. A new approach to linear filtering and prediction problems[J]. J. Basic Eng. Trans. ASME, 1960, 82(1):35-45.
[18] LI K, THOMPSON S, PENG J. Modelling and prediction of NO_x emission in a coal-fired power generation plant[J]. Control Engineering Practice, 2004, 12(6):707-723.

Fusion Measurement Method for NO_x Concentration of Flue Gas Based on Kalman Filter

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