Research And Development Of Burning Zone Status Recognition System For Alumina Rotary Kiln Process Based On Image Processing

China has plenty of alumyte, but 80% of alumyte reserves is of low alumina silica ratio. Rotary kiln is the core equipment in alumina sintering process and its main function is to sinter raw material slurry to produce qualified alumina clinker. Sintering status of rotary kiln synthetically reflects the burning state and material sintering status, and tightly correlates with alumina clinker quality, production, energy consumption as well as equipments status etc. Its automated recognition is important for automation of rotary kiln process.Rotary kiln is about one hundred long, because of its special architecture and complexity of sintering process, the liter weight of clinker in the alumina sintering process is hard to directly measured online, and has many integrated complexity such as serious disturblance in temperature detection of burning zone, multi-variables strong coupling, strong nonlinearity, big inertia as well as uncertain disturbance etc.Sintering status of rotary can not be exactly measured continuously on-line. Because of flame flickering, material moving and convection as well as radiation exchange of heat which produced by inclined body and rolling of rotary kiln, as well as complex solid state, liquid state and gaseous physical chemical reactions with high temperature, flame area and material area in burning zone of rotary kiln is difficult to discriminate and disturbance in process data detection is serious, sintering status can not be exactly recognized by routine instrument. Alumina rotary kiln process control has depended on "man-watch" operation for a long time, which observes flame combustion state and material sintering status by eyes, and combines process data to recognize sintering status. As a result, over-burning or under-burning usually happens, the qualification index of liter weight of clinker is low, kiln liner is easy to wear out, the kiln running rate and yield is low; the energy consumption and labor intensity remains high; and environmental pollution is serious.This work is supported by the program of "Optimizing control techniques for large rotary kiln processes", which is a sub-project of the National Hi-tech 863/CIMS Program named "Overall project design and key technology development of the integrated automation system of China Aluminium Corporation". In order to realize the automated sintering status recognition of rotary kiln, this dissertation has made detail work on the research and development of burning zone status recognition system of alumina rotary kiln, The detail works are summarized as follows:1. Bases on "man-watch" recognition experiences, image processing technique has been combined with data fusion technique and a sintering status recognition method ground on features of sintering status image and process data fusion has been proposed which include image pre-processing, image segmentation, image features extraction, data fusion and sintering status recognition model.Consideing frequency characteristic of noise of sintering status image of alumina rotary kiln and the complexity of color image processing algorithm which is a time-cosuming algorithm, an integrated pre-processing algorithm is proposed for sintering status image with frequency filter technique and gray transform technique. Frequency noise can be removed from sintering status image by this algorithm. As a result, a filterd gray sintering status image is abtained.Because of difficulty of segmentation between flame area and material area of sintering status image based on different gray levels of the pixels, the texture difference between flame area and material area of sintering status image of alumina rotary kiln has been synthesized and a segmentation algorithm which uses texture coarseness that is depicted by Gabor wavelets is proposed to improve the fuzzy clustering result of gray levels, as a result a exact segmentation of sintering status image is abtained.Depending on "man-watch" experiences, five features of sintering status image of alumina rotary kiln is depicted, such as height of material, flickering frequency, average gray of whole image, color of flame as well as color of material, and the feature exaction algorithms for the above features from the whole image as well as segmented image areas are proposed.Refering to "man-watch" recognition process, a data fusion algorithm which is made up of data filtering, synchronization and standardization is proposed with the characteristics of features of sintering status image and key process data such as temperature of burning zone, temperature of head of rotary kiln, temperature of end of rotary kiln to as well as current of cooler,as a result, a hybrid data features is abtained.A recognition model of sintering status based on quasi-symmetrical binary tree svm(support vector machine)constructed with hybrid data features as input and over-sintering, just-sintering and under sintering status as output. An automated algorithm for sintering status recognition is proposed based on the above model. In order to adapt to the change of production boundary conditions, professional modified samples is used to realize feedback increment learning for recognition model. 2. A status recognition software based on the above algorithms has been and developed, which realizes four functions, such as video surveillance of sintering status, recognition of sintering status, process data communication and human-machine interaction. a remote distributed system hardware platform has been constructed which is made up of fore-end image capture equipment, network video transformation equipment, image capture card, industrial control computer, display and storage. The above hardware, operation system of computer, API (Application Programming Interface), COM (Components Object Model), DLL (Dynamic Link Library), SDK (Software Development Kit) and recognition software of sintering status constitutes the recognition system of sintering status of alumina rotary kiln.3. The experiment which compares recognition method proposed by this dissertation with the status recognition method based on pattern recognition of temperature of burning zone shows that this method which based on features of sintering status of alumina rotary kiln and process data fusion can recognize the sintering status correctly. Carried out system experiment research on 3# rotary kiln in Shanxi alumina factory, the result shows that the recognition system can realize the designed function, run reliable and steady and recognize sintering status in real-time, accurate recognition rate is up to 93.5%.