摘要
为提高磨煤机系统运行的安全性和经济性,建立了MPS型中速磨煤机的动态数学模型.基于质量和能量平衡,考虑原煤水分对能量平衡的影响并把煤粉水分作为一个状态输出量,利用历史数据结合遗传算法对模型中的参数进行辨识,并通过扩展卡尔曼滤波方法对磨煤机内部状态进行估计,利用2组现场历史数据对模型状态和模型输出进行验证.结果表明:利用扩展卡尔曼滤波方法估计得到的状态与模型状态基本吻合,模型输出与实际输出有良好的一致性,所建立的磨煤机模型能够对实际磨煤机的动态性能进行有效预测.
To improve the safety and economy of coal mill operation, a dynamic mathematical model was es- tablished for MPS medium speed coal mill based on mass and energy balance. Considering the impact of coal moisture on the energy balance and taking the pulverized coal moisture as a state output, the model parameters were identified with historical data using genetic algorithm, and the internal states of coal mill was estimated by an extended Kalman filter (EKF) method, while the model states and outputs were vali- dated with two sets of on-site data. Results show that the states estimated by EKF method are consistent with the model states, and the model outputs are in good agreement with those of online measurements, which prove the coal mill model established to be effective in predicting the dynamic performance of coal mills.
出处
《动力工程学报》
CAS
CSCD
北大核心
2015年第1期55-61,共7页
Journal of Chinese Society of Power Engineering
基金
国家科技支撑计划子课题资助项目(2011BAA04B03)