无迹卡尔曼滤波算法在污水处理BSM1过程中的应用研究

APPLICATION AND RESEARCH OF UNSCENTED KALMAN FILTER ALGORITHM IN BSM1 PROCESS OF WASTEWATER TREATMENT

污水处理过程具有多变量、强非线性和强扰动等特性,且系统输入具有随机性,不同天气状况和不同时间段的污水的排量不同。扩展卡尔曼滤波存在估计精度低和鲁棒性差等缺陷,当系统模型参数变化和外界环境噪声较大时,扩展卡尔曼滤波估计性能下滑。将无迹卡尔曼滤波算法应用到污水处理系统中,并与扩展卡尔曼滤波算法相比较,结果显示,无迹卡尔曼滤波可以对污水系统运行的实际状态进行更好的估计。该方法不仅提升了估计精度,更提高了估计的鲁棒性。

The wastewater treatment process has the characteristics of multivariable,strong nonlinearity and strong disturbance,and the input of the system is random,the sewage discharge is different in different weather conditions and different time periods.The extended Kalman filter has some defects such as low estimation accuracy and poor robustness.When the system model parameters change and the external environment noise is large,the estimation performance of the extended Kalman filter decreases.In this paper,the unscented Kalman filter algorithm was applied to the wastewater treatment system.The analysis results show that the unscented Kalman filter can better estimate the actual state of the sewage system.This method not only improves the estimation accuracy,but also improves the robustness of the estimation.