多传感器数据融合技术在锅炉热效率计算中应用研究

Research and application of multi-sensor data fusion techniques in calculation of boiler thermal efficiency

实现锅炉热效率实时计算对优化锅炉燃烧经济性具有重要意义,其难点在于飞灰含碳量的在线测量,目前广泛采用微波飞灰测碳仪进行在线监测。然而在实际中烟气密度和流速对仪器的测量精度有重要影响,使测量结果产生较大波动。本文设计了对烟气密度和流速的测量方法,并构造了基于多传感器数据融合技术的测量系统,利用BP神经网络对多传感器信息进行有效融合,在一定程度上提高了飞灰含碳量的测量精度。进而通过采集排烟温度、氧量等运行参数,依据反平衡方法给出锅炉效率的在线计算模型。

Realization of boiler efficiency online calculation is significant to optimize the combustion economization of boiler. But unburned carbon content in fly ash is difficult to measure online. Microwave online measurement system is widely used nowadays, however, practically smoke density and velocity have significant influence to the measurement accuracy and it lead great fluctuation ofmeasurementresults. In this paper, measurement method ofsmoke density and velocity is designed and the measurement system based on multi-sensor data fusion technique is structured. BP neural network is utilized to fuse the multi-sensor information effectively. This method have improved measurement accuracy of unburned carbon content in fly ash at a certain extent. Through gathering operating parameters, such as flue gas temperature, oxygen content, a boiler efficiency online calculating model according to redirect heat balance method is presented.