Research On Behavior Characteristics And Control Intelligence Of Flotation Bubbles Based On Image Processing

In the process of coal flotation,the size and stability of foam/bubble affect the yield and ash content of cleaned coal.Although flotation technology has made new development,further research is needed in the recovery of fine materials and intelligent control flotation.The core of these two aspects of research is bubbles,so studying the behavior characteristics of bubbles can serve both the optimization of flotation efficiency and intelligent control of flotation.In order to intelligently control the flotation process and recover fine clean coal,this dissertation realizes the collection,recognition,segmentation and extraction of effective eigenvalues of bubble/foam images by building experimental platforms such as image recognition system,bubble/foam generation system,flotation optimization and control system,and then analyzes the relationship between bubble behavior characteristics,foam stability and flotation optimization and control,and uses quantum mechanics and molecular dynamics to simulate coal,kaolin The microscopic interface between foaming agent,kerosene and bubbles is analyzed to analyze its mechanism.Finally,an expert system is established with the extracted value of foam image as the characteristic data and the ash content of clean coal to realize the intelligent flotation control.Based on the above research,the main conclusions are as follows:(1)A foam/bubble image processing system was built,and a processing method of limited contrast adaptive equalization enhancement and bubble image segmentation based on marker positioning watershed was proposed to extract the eigenvalues of bubbles/foam,and was applied to experimental scale and industrial image information processing.The results show that the image processing method can effectively extract the eigenvalues of foam images.(2)The characteristic values extracted from the image are used for secondary information reconstruction,and the behavior of bubbles and the stability of foam are experimentally studied.In terms of the geometric behavior of bubbles,the diameter of bubbles/foam is a single peak(the inflation rate is less than 0.8 L/min,and the size is 1.5L/min)or a bimodal distribution(the inflation rate is between 0.8 and 1.0 L/min);The probability distribution of bubble diameter is one or two segments of non normal Gaussian function.In terms of the movement behavior of bubbles,the movement rate of bubbles fluctuates between 0.20 m/s and 0.5 m/s,rooted in the deformation and forced vibration of bubbles.The stability test of foam is carried out with inflation rate(1.6 – 4.8 L/min),foaming agent concentration(0.03% – 0.07%)and coal/kaolin concentration as parameters.The test results show that the stability factor of foam is the largest when the concentration of sec-octanol,terpineol and Tween 80 is at the critical micelle concentration.The air-introduced flotation test is achieved by adjusting the inflation rate and inflation nozzle of the bubble distributor.The analysis of experimental results shows that the best flotation effect is achieved when the stirring rate is 2200 r/min,the diameter of the inflation tube is 0.7 mm,and the air entrainment rate is 5.0 L/min.The data analysis results show that the recovery rate of fine coal particles increases.(3)Using quantum mechanics and molecular dynamics to simulate the micro interface interactions between foaming agents(sec-octanol,terpineol,Tween 80),collectors(kerosene),coal,and kaolin.Based on the phase analysis of experimental coal samples,the content of each component in the coal was determined by XRD,infrared,nuclear magnetic resonance spectroscopy,and XPS,and a molecular model of coal was established to analyze the interface interaction configuration between coal and collectors.The covering effect of collectors and the bridging effect of foaming agents were analyzed at the micro level through the adsorption relationship between polar groups such as N,S,O and kerosene,sec-octanol,Tween 80,and terpineol.(4)An intelligent coal slurry flotation system was built based on image processing and applied it to the intelligent control of the Xiqu Coal Preparation Plant.The ash content of flotation clean coal is predicted by BP neural network training based on the bubble area,foam bearing capacity,gray variance,smoothness and skewness,and the ash content of clean coal.The error between the measured ash content in production and the predicted ash content is within ±0.5.The intelligent flotation control based on foam bubble image information extraction is realized.