Research On Quality Evaluation Method Of Molybdenum Ore Flotation Process Based On Foam Image Processing

As a mineral resource,molybdenum is widely used in all walks of life.This is due to its unique properties such as high strength,wear resistance and high melting point.The current flotation production status and recovery rate have gradually failed to keep up with the huge market.Therefore,it is necessary to control the flotation quality of molybdenum ore in time to provide a basis for better control of the recovery rate.Froth flotation is a method that separates minerals of different components according to the physical and chemical differences of the surface of the ore particles and the addition of agents,and finally obtains the concentrate containing high components and tailing with low useful components.The traditional manual control mode has many drawbacks such as inaccuracy and subjectivity,which can no longer meet the needs of flotation process control and quality management.This paper is based on the actual production data of industrial sites,combined with image processing technology and random forest classification algorithm to realize the quality evaluation of molybdenum ore based on foam images.The main contents are as follows:(1)Aiming at the problem of quality evaluation of the flotation process,the paper combs the literature from the perspectives of flotation index prediction,working condition recognition,image processing and classification and evaluation algorithms,and summarizes the technology and theoretical methods provided by the current research institute to explore flotation for this article The rapid quality evaluation method provides strong support.The principle of flotation and the process parameters that affect its performance are analyzed,and the correlation between process operating parameters and froth image parameters and flotation state indicators is sorted out.(2)In terms of feature extraction of foam images,three elements of the HSV color model are used: brightness,hue,and saturation to represent color features;four secondary statistics based on gray space matrix energy,entropy,contrast,and correlation are used to represent color features.Express the texture characteristics;reduce the noise of the foam image,and use the Sobel operator to detect the edges of the bubbles in the flotation foam image,use the morphological marking method to mark the foam area,and extract the size characteristics of the flotation foam by calculating the area and circumference of the foam.(3)According to the image processing algorithm,the feature value of the foam image is extracted,the correlation analysis method is used to determine the key features that affect the recovery rate,the random forest algorithm is used to train the image feature data set,and the selected key image features are used as the input of the model,and the quality result is used as the input of the model.Output and optimize the two parameters of the algorithm to achieve the best classification accuracy of the algorithm.The random forest classification algorithm is used to classify the quality of flotation to establish a rapid evaluation model of flotation quality.(4)Apply the molybdenum ore flotation quality evaluation method based on the random forest algorithm to a flotation workshop of Luoyang Molybdenum Industry.The image features and recovery data extracted by the image acquisition system are divided into training set and test set,and input The model obtains the flotation quality evaluation results of molybdenum ore.The support vector machine is selected as the comparison algorithm for comparison.The results show that the classification accuracy of the random forest algorithm is as high as 98%,which is much higher than SVM,which realizes the effective classification and rapid evaluation of flotation quality.