A method for predicting coal particle ash content by image analysis
An image analysis and coal particle technology, applied in image analysis, analysis materials, image data processing, etc., can solve the problems of coal particle ash deviation, small error, inability to understand coal particle ash content in time, and achieve timely and accurate quality. Effect
Inactive Publication Date: 2011-12-28
CHINA UNIV OF MINING & TECH
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[0002] At this stage, there are mainly two methods for measuring the ash content of coal particles: one is to determine by sampling and burning ash. This method has the highest reliability, but it takes about 3 hours to measure the ash content once, which has a hysteresis and cannot timely understand the coal particles. The second is to use a radioactive online ash meter for measurement, which has high timeliness, but if the coal particles contain certain minerals, the ash content of the coal particles will have a large deviation; in fact, many experienced workers in coal preparation plants can use The range of ash content of coal particles is judged by the naked eye, and the error is not large. They judge based on factors such as the color and brightness of coal particles. invented this method of predicting the ash content of coal particles by image analysis
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[0008] The hardware structure of the coal particle image analysis system is shown in the accompanying drawing, which is mainly composed of coal particle 1, image collector 2, data line 3, and computer 4. The image collector 2 is used to take images of coal particles 1, and the computer 4 controls the image collector 2 to take pictures through the data line 3, and then transmits the image information to the computer 4 through the data line 3 for storage.
[0009] The system software can identify and separate the coal particles in the image by color segmentation, so as to obtain the true color image of the coal particles.
[0010] The color characteristics of coal particles are obtained through two different reference systems: (1) RGB (red, green, blue), (2) HSI (hue, saturation, lightness).
[0011] The color moment method believes that the color information is concentrated in the low-order moment of the image color, and they mainly count the first-order, second-order and third...
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A method for predicting the ash content of coal particles through image analysis, the purpose of the invention is to realize a coal particle ash measurement method with high timeliness and stable accuracy. The present invention adopts an image collector, a computer and its auxiliary parts to form a hardware platform. After acquiring the image of coal particles, the coal particles are identified and separated one by one by using the coal particle automatic recognition software, and then the coal particle feature extraction software is used to extract the characteristics of the coal particle image. parameters, and finally the neural network is used to predict the ash content of coal particles. The system can be used to predict the ash content of coal particles, so as to understand the quality of coal more timely and accurately, and provide a basis for the next step of production.
Description
Technical field [0001] The invention relates to a method for predicting the ash content of coal particles through image analysis, belonging to a mineral detection method. Background technique [0002] At this stage, there are mainly two methods for measuring the ash content of coal particles: one is to determine by sampling and burning ash. This method has the highest reliability, but it takes about 3 hours to measure the ash content once, which has a hysteresis and cannot timely understand the coal particle ash content. The second is to use a radioactive online ash meter for measurement, which has high timeliness, but if the coal particles contain certain minerals, the ash content of the coal particles will have a large deviation; in fact, many experienced workers in coal preparation plants can use The range of ash content of coal particles is judged by the naked eye, and the error is not large. They judge based on factors such as the color and brightness of coal particles....
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Inventor 杨建国王羽玲张泽琳
Owner CHINA UNIV OF MINING & TECH