基于过程模型的化工动态数据校正方法研究  

Studies on Chemical Dynamic Data Reconciliation Method Based on Kalman Filter

作　　者：李贵晓 黄兆杰[1] 金思毅[1] 

机构地区：[1]青岛科技大学化工学院,山东青岛266042

出　　处：《青岛科技大学学报（自然科学版）》2015年第3期297-301,共5页Journal of Qingdao University of Science and Technology:Natural Science Edition

基　　金：重质油国家重点实验室开放课题基金资助项目(201103004)

摘　　要：在工业过程中,获得准确可靠的测量数据是实现过程控制、模拟、优化和生产管理的前提条件。当测量数据中存在过失误差时,基于过程模型的卡尔曼滤波得到的校正结果准确性会降低。为了降低过失误差的影响,将鲁棒估计函数与卡尔曼滤波相结合,利用鲁棒函数的影响函数修正测量值方差,提出了基于鲁棒估计函数改进的卡尔曼滤波,并推导给出了修正方差的计算公式。动态非线性实例的应用结果表明,与传统的卡尔曼滤波相比,改进的卡尔曼滤波的过失误差校正性能有了显著提高,可有效地用于动态过程的数据校正。For process industries, it is crucial for the control, simulation and manage- ment of the process to obtain high quality and reliability data. When the gross error ex- ists in the measured data, the data correction performance of Kalman filter based on process model will decline. In order to reduce the influence of gross errors, in this pa- per, an improved Kalman filter based on the robust estimation function is proposed; al- so the calculation formula for modified Kalman filter is deduced. By the proposed meth- od, the influence function of robust estimator is used to modify the measurementrs vari- ance, as a result the difference between the corrected value and the measurement contai- ning gross error increase, and the influence of gross errors is reduced. The simulation results of a nonlinear dynamic instance show that, compared with the traditional Kalman filter, the improved Kalman filterts gross error correction performance has significantly improved, therefore, the proposed modified method can be effectively used for dynamic process data reconciliation.

关 键 词：过失误差侦破 卡尔曼滤波 鲁棒估计函数 动态数据校正 

分 类 号：TQ056[化学工程]