Maintaining a normal burning temperature is essential to ensuring the quality of nonferrous metals and cement clinker in a rotary kiln. Recognition of the temperature condition is an important component of a temperature control system. Because of the interference of smoke and dust in the kiln, the temperature of the burning zone is difficult to be measured accurately using traditional methods. Focusing on blurry images from which only the flame region can be segmented, an image recognition system for the detection of the temperature condition in a rotary kiln is presented. First, the flame region is segmented employing a region-growing method with a dynamic seed point. Seven features, comprising three luminous features and four dynamic features, are then extracted from the flame region. Dynamic features constructed from luminous feature sequences are proposed to overcome the problem of mis-recognition when the temperature of the flame region changes rapidly. Finally, classifiers are trained to recognize the temperature state of the burning zone using its features. Experimental results using real datasets demonstrate that the proposed image-based systems for recognizing the temperature condition are effective and robust.

Recognition of the Temperature Condition of a Rotary Kiln Using Dynamic Features of a Series of Blurry Flame Images

Rotary Kiln Combustion State Recognition Based on Convolutional Neural Network

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.

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

This paper presents a potential application of texture analysis and classifier for a set of sintered clinkers in rotary kiln. This approach, which consists of the extraction of clinker from the whole image, GLCM texture features computation, and C4.5 classification, provides an efficient way for untouched on-line analysis of sintered clinker for the automatic control of industrial rotary kiln. 69 clinker images from an alumina rotary kiln were employed, 5 sets of 14 texture features were computed based on GLCM of different distances, texture feature classification were achieved by C4.5 algorithm, all graphic illustrations of classification rule are given as a function of texture ID. Experimental results demonstrate that it is effective to classify these sintered clinkers of different qualities. This paper may provide a basis for analyzing other kinds of industrial materials in rotary kiln.

Texture analysis and classification for clinker in rotary kiln

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Texture analysis and classification for clinker in rotary kiln