Single-step prediction method of burning zone temperature based on real-time wavelet filtering and KELM

摘要

The single-step prediction of burning zone temperature plays an important role in the safety and stability control of cement rotary kiln. This is because, the abnormal temperature events can be found as early as possible and the operator can take effective emergency measures in time. In this paper, the burning zone temperature single-step prediction method based on real-time wavelet filtering and kernel extreme learning machine is studied. Firstly, the visual inspection device is used to detect the burning zone temperature. And then, the amplitude limited filtering method is used to weaken the effects of temperature anomalies. On this basis, the real-time filtering of the burning zone temperature is realized by combining the sliding time window and wavelet filtering method. After that, the single-step prediction of burning zone temperature is realized by combining the sliding time window and kernel extreme learning machine method. At last, the burning zone temperature prediction method is validated. The minimum root mean squared error of the 5 consecutive days is0.4259°C. The single average running time of model training and prediction of kernel extreme learning machine is much less than support vector regression, which is very helpful for the online prediction of burning zone temperature. The result shows that the burning zone temperature single-step prediction method proposed in this paper is feasible and effective.