Relationship Between Froth Features And Grade Of Copper Sulfide Concentrate Based On Color Image Processing

Copper concentrate grade is not only an important parameter in copper sulfideproductive process, but also a significant quality index of flotation product. There are manyshortages in copper concentrate grade detection methods, such as strong subjectivity, lowaccuracy, long detection period, data correction, high cost and so on. Copper sulfide was usedas an object of study in this paper. Based on color image processing methods, color imagefeature of copper sulfide concentrate was systematically studied in solid phase, solid-liquidphase, gas-solid-liquid phase environment. Relations between image feature and copperconcentrate grade was revealed and concentrate grade prediction model based on imagefeature was developed. This research could provide theoretical support for further study aboutcopper concentrate grade detection methods and technical support for process control inflotation. Main research contents and conclusions are as follows:Grade prediction model based on color microscopic image feature of copper sulfideconcentrate powder was developed. For the problems of fine particles in copper sulfide testsamples, a color microscopic image acquisition device was built, then a hue-preserving colorimage enhancement method was proposed to denoise and enhance color microscopic imageeffectively. Color microscopic image features such as color vector angle, average red, green,blue, and hue values were extracted by statistical approach. Furthermore, three concentrateprediction models were developed, base on LS-SVR method and image color feature.Comparative results of three models’ predictive performance indicated that copperconcentrate grate prediction model based on average hue value was an optimal model.Grade prediction model based on color image features of copper sulfide pulp wasconstructed. For image acquisition problem of copper sulfide pulp, a pulp color imageacquisition device and method was designed. Cropping and enhancing preprocess method forpulp image was studied. Color features of pulp image were extracted by color ratio andrelative color degree methods. Tamura method was firstly introduced to extract texturefeatures from V component, then dimensionality reduction for color and texture features wasconducted through correlation coefficient method. Moreover, linear and non-linear relationsbetween pulp image features and copper grade were studied by multiple linear regressionmethod and GRNN method. Study results showed that grade prediction model based onGRNN method had higher prediction accuracy than model based on multiple linear regressionmethod, and grade prediction model, based on GRNN method and texture features of pulpcolor image, was an optimal model. Copper concentrate grade soft-sensor models based on froth color image features incopper sulfide flotation process were developed. A video-image acquisition device forflotation froth was developed in order to accomplish color image task of rougher froth andcleaner froth of copper sulfide. Preprocess methods for rougher and cleaner froth, such ascropping, deblurring, denoising and enhancement, was studied. In addition, color histogram,color moments, relative color degree methods were used to extract color features from frothcolor image. And texture features of H, S and V component were extracted by Tamura methodand WPT combined with Tamura method. Then, dimensionality reduction method, which wasbased on multiple clustering method and Lasso method, was applied to image featureparameters. Combined with hard threshold method of correlation coefficient, secondaryvariable of soft sensor model was selected. Finally, through multiple linear regression method,PLS method and LS-SVR method, soft sensor model of copper concentrate grade weredeveloped, which based on image features of rougher and cleaner froth. Comparative resultsof these models’ predictive performance, results showed: in copper sulfide rougher flotationprocess, the soft sensor model of copper concentrate grade using LS_SVR method was anoptimal model, which was based on color and texture features of rougher froth image; incopper sulfide cleaner flotation process, the soft sensor method of copper concentrate gradeusing LS_SVR method was an optimal model, which was based on color features of cleanerfroth image; it could be concluded the grade of copper concentrate would be predicted byusing color image processing method.