Optimization of Soot Blowing of Coal-fired Boilers Based on UKF Predicted Health Status

Abstract

Aiming at the problems of boiler heating surface from clean to produce ash and slag, the heat transfer efficiency of boiler is reduced. With the cleanliness factor as the monitoring index, a real-time soot blowing prediction method based on unscented Kalman filter algorithm is proposed. The cleanliness factor degradation data was analyzed by double exponential function fitting, and the model parameters were updated by the unscented Kalman filter algorithm, and the future trend of the cleanliness factor was predicted. At the same time, a soot-blowing optimization model with the largest heat transfer per unit time is proposed to further optimize the soot blowing time. Taking the cleanliness factor data of a economizer as an example, by comparing with the extended Kalman filter algorithm, it is found that the proposed method can predict the soot blowing time more accurately, and the calculation of the soot-blowing optimization example is carried out to verify the feasibility of the proposed optimization model.