基于EKF技术的CFB锅炉残碳量估计

Application of the Extended Kalman Filtering Technology in the Estimation of the Residual Carbon Content of a CFB Boiler

针对循环流化床锅炉残碳量无法直接测量的问题,提出了利用容易测量的风量和煤量信息,间接测量残碳量的方法。首先通过机理分析,建立残碳量动态机理模型,能够实时反映炉膛内的燃烧状况,然后基于广义卡尔曼滤波(EKF)理论建立了信息融合算法,通过给煤量、总风量等信息准确估算出炉膛内的残碳量。最后通过对某电厂300MW机组进行实验论证,根据残碳量计算的热量、床温、氧量和实际值对比验证,负荷在95%～65%的范围内变化时,误差不超过2%,证明循环流化床锅炉中残碳量可以通过广义卡尔曼滤波信息融合方法准确预测。

In the light of the problem that it has no way to directly measure the residual carbon content of a circulating fluidized bed boiler,presented was a method for indirectly measuring the residual carbon content by using the information about the air quantity and coal quantity which can be easily measured. First,through an analysis of the mechanism, established was a model governing the dynamic mechanism of the residual carbon content,capable of real-time reflecting the combustion conditions inside the furnace. Then,based on the generalized Kalman filtering theory,an information fusion algorithm was determined and the coal quantity and total air quantity fed were used to accurately calculate the residual carbon content inside the furnace. Finally,a test verification was performed on a 300 MW unit in a power plant and a contrast of the heat quantity,bed temperature and oxygen quantity thus calculated according to the residual carbon content with the real values showed that the error does not exceed 2% when the load changes in the range from 95% to 65%,proving that the residual carbon content in a circulating fluidized bed boiler can be accurately predicted by using the generalized Kalman information fusion method.