Study On Bubble Dynamic Characteristics In Flotation Process Based On Valley Point Detection And Contourlet Transform

There are billions of tons of ore and materials processed by flotation every year around the world.Workers operating the flotation according to personal experience will not only make the flotation effect difficult to guarantee,but also cause the mineral resources and pharmaceutical waste,increasing production costs and reducing business competitiveness.Therefore,it is necessary to solve the status that the flotation monitoring mainly depends on artificial vision for years.With the rapid development of computer,the digital image processing technology is widely used.Image processing technologies such as Contourlet transform and characteristic point detection are used to analyze the dynamic characteristics of bubbles,and to establish the linkages between these characteristics parameters and the flotation indexes.The abnormal situation in the flotation process is reflected in timely based on the evaluation of fuzzy mathematics.The main research content and the contributions of this thesis can be summarized as:1.Bubble bottom image extraction.In view of the problem that the extraction process of flotation bubble boundary is complicated,the Contourlet transform is used to extract the bubble boundary from flotation images in the frequency domain to form the bottom image.The paper focuses on the components of the bottom image which is adjusted accurately by changing the Contourlet transform parameters.After the exact adjustment of the parameters,the extracted bubble bottom image by Contourlet transforms does not contain the information such as bright spots and dark spots which are difficult to remove.This method saves time and keeps the edge details as much as possible which provide the more effective information for the subsequent key points matching work.2.Key points match of bubbles.The SIFT algorithm is used to match the key points of bubble bottom images of flotation.For the problem of the complex calculation,it sets a different search interval for SIFT according to bubble velocity.In addition,the use of bubble bottom images led to the resolution of the difficult problem that light noise(bright spots and dark spots)are mistakenly matched,but some noise(except light noise)difficult to remove will seriously affect the judgment of the bubble motion characteristics.Based on that,the slope discrimination method and the RANSAC algorithm are used to eliminate the noise matching points.The advantage of the RANSAC algorithm is that it estimates steadily model parameters.However the computational complexity is relatively large.Therefore the slope discrimination method is added to compensate for this problem.3.Extraction and optimization of bubble velocity.The bubble velocity characteristic data are obtained by the method based on the valley detection and Contourlet transform.Then the data are optimized by Kalman filter which improve the accuracy of the data for flotation.It mainly studies the main noise source of flotation and the input of Kalman filter in the case of the complicated flotation velocity model.After optimized by Kalman filter,the flotation bubble acceleration is more stable which is in line with the actual situation.4.Evaluation of flotation status.Based on the analysis of flotation bubble characteristics including bubble velocity,size and texture,the flotation status is determined by the fuzzy mathematics which can objectively construct the flotation state data.And the changing process can be accurately expressed by the chart,which makes the analysis result more easily fed back to the adjustment scheme and easy to be understood and implemented by machine.