Recognition of sintering state in rotary kiln using a robust extreme learning machine

Sintering is a key process for the industrial clinker production. The sintering state estimation in clinker is an essential factor for its process control. In this paper, a feature extraction method from flame image and a robust extreme learning machine (RB-ELM) classifier are provided to recognize sintering process in rotary kiln. After a preprocessing of image denoising and illumination compensation, material region of flame image is segmented by region growing algorithm and a 5-D statistic feature vector is extracted from it for the following classifier. In order to reduce the influence of outliers in training data caused by blurring image and to achieve a real-time application on site, a robust extreme learning machine, which adopted iterative weight least square (IWLS) method based on M-estimator, is used for fast classification of sintering state. Experimental results show that the proposed method can recognize sintering state accurately, quickly and robustly.