基于改进粒子滤波算法预测健康状态的锅炉吹灰优化

为了对锅炉受热面沉积的灰污进行及时、准确地清扫,制定合理的吹灰优化策略。采用清洁因子来评估受热面的健康状况,并根据实时监测数据结合改进粒子滤波;采用滚筒式预测单次积灰过程内清洁因子未来的变化趋势,并验证了该预测方法的准确度。同时,提出一种基于单位时间传热量损耗最低的吹灰优化模型,吹灰优化计算中采用相同工况下多组清洁因子数据进行拟合。以某300 MW燃煤锅炉为例,验证了所提优化模型的可行性。

Soot Blowing Optimization based on Improved Particle Filter Algorithm to Predict Health Status

In order to timely and accurately clean the ash pollution deposited on the heating surface of the boiler,a reasonable soot blowing optimization strategy is formulated.The cleaning factor is used to evaluate the health of the heated surface,and the particle filter is combined with the real time monitoring data.The future trend of the cleaning factor in the single ash deposition process is predicted by the drum type,and the accuracy of the prediction method is verified.At the same time,a soot blowing optimization model based on the lowest heat loss per unit time is proposed.In the soot blowing optimization calculation,multiple sets of cleaning factor data under the same working conditions were used for fitting.Finally,a 300 MW coal fired boiler is chosen as an example to verify the feasibility of the proposed optimization model.