Research Of The Bubble Image Processing System In Coal Slurry Flotation Based On LabVIEW

Ash content of clean coal and state of bubble layer would be chose first as the real-time basis to judge the effect of flotation in coal preparation plant. However, there exists a lag in the measurement of ash content of flotation clean coal, which makes it impossible for the real-time monitor of flotation indexes. Thus, workers in plants often watch the quality of flotation bubbles to estimate ash content and yield of clean coal, and then to change operation parameters of flotation. However, when it comes to judge the state of bubble layer by eyes of workers, there may be some different results sometimes, due to the difference in workers' experience, which is often interfered by individual subjective factors. Thus, if “camera” could replace human eyes to judge images captured, not only could it achieve the real-time monitoring of coal slime flotation indexes, but also it could improve the accuracy of coal slime flotation and reduce the overload of labor workers. Therefore, the introduction of machine vision development is of great significance to the development of coal flotation coal industry. The main contents of this paper include the following aspects:According to the research status of machine vision technology in home and abroad as well as the specific cases applied in mineral flotation industry, the feasibility of image processing system applied in coal slime flotation was analysed. Based on the characteristics of the program development environment, the system development platform is identified to be Laboratory Virtual Instrument Engineering Workbench(LabVIEW)According to the basic configuration of machine vision and together with the actual situation of the flotation plant of the coal preparation plant, the comparison and discuss were made in the aspects of PC types, the specific version and performance of cameras and lens, light source and lighting system, and then a hardware system of image processing was developed.The software system is divided into 5 modules according to the thought of modular programming, and the function of each module and the preparation process are discussed in this paper. The paper focus on the development of image preprocessing and processing, through the comparison of image segmentation results of three different algorithms, and ultimately Power1/X- Smoothing-Median watershed segmentation is selected to be the core algorithm of this research.The development of coal flotation foam image processing software were used in the flotation workshop of coal preparation plant in Shanxi and Shandong province separately for on-site inspection, image acquisition sampling are made in accordance with the sampling standard and the corresponding moment of fine coal content ash are measured. Using image processing software to extract the three flotation foam characteristic parameters, which are the mean gray, bubble number and bubble diameter, and established flotation concentrate ash prediction model based on the three characteristic parameters using BP neural network modeling method. Then the test is made to validate the model and the results show that the average relative deviation absolute value of predicted and actual ash content was 4.94% and 3.17%.