基于多传感器融合技术的飞灰含碳量测量

飞灰含碳量是衡量火力发电厂锅炉燃烧经济性的一项重要指标,目前广泛采用微波飞灰测碳仪进行在线监测。但在实际应用中灰种变化对仪器的测量精度有严重影响,该文针对火电厂飞灰含碳量的测量受灰种影响较大这一技术问题,分析了影响微波飞灰含碳量测量精度的因素,采用微波技术、电容技术,结合飞灰比电阻特性构造了多传感器融合的测量系统,利用BP神经网络对多传感器信息进行有效融合。试验结果表明,该方法可以在一定程度上降低灰种变化对飞灰含碳量测量结果的影响,并可提高对同一灰种的测量精度。

Measurement for Unburned Carbon in Fly Ash Based on Multi-sensor Fusion Techniques

Unburned carbon content in fly ash is an important index for the Thermal Power Plant. Microwave online measurement system is widely used nowadays, however, coal type has great influence on its accuracy. Based on analyzing the factors mainly affect the accuracy of the measurement, microwave technique and capacitance technology which combined with the specific resistivity of fly ash is adopted to construct a multi-sensor fusion-based measuring system, and then BP neural network is utilized to fuse the multi-sensor information effectively. Experiment results have shown that the proposed method can effectively decrease the coal type effect on the unburned carbon measurement, and at the same time the accuracy for the same type of coal is increased.