Statistical Modeling Of Froth Images And Its Applications In The Monitoring Of The Mineral Flotation Process

Froth flotation is the most important mineral separation technology, which is used to concentrate the valuable minerals from the source ores according to the different surface hydrophobicities of the mineral particles based on the principle of surface hydrophobicities. Though one hundred years have elapsed, the automatic monitoring of the flotation process is still hard to be put into effect, resulting in low recovery of the mineral resource with fluctuating product indexes, sine the practical industrial flotation process usually consist of long process circuits and great amount of influence factors with unclear inner mechanism. In view of the merits of the industrial vision monitoring system, which responses fast and provides objective and non-intrusive monitoring of the froth states, machine vision based flotation process monitoring is recently expected as a promising tool to improve the online detection, measurement and control means and ultimately to achieve the optimal control the flotation process by both the scientific researchers and industrial engineers. In the machine vision based flotation process monitoring, researching the proper froth image processing methods and extracting the effective bubble features closely relating to the production condition of the flotation process are the prerequisites for subsequent flotation process modeling and optimization. However, the traditional image processing and analysis methods are difficult to be used in the froth image analysis and feature extraction due to the random accumulation of the mineralized bubbles, which have diverse morphological structures without background between each other. In order to quantitatively delineate the randomly accumulated bubbles with various shapes and sizes and obtain their comprehensive visual features, which do not depend on the single bubble in the froth surface, the methods such as probability theory, statistical learning and pattern recognition are applied to the froth image analysis and processing. The statistical distribution models of the froth image are established in the image transform domain according to the statistical distribution profile of the random signal characteristics for the subsequent image analysis and understanding to provide effective prior knowledge. The statistical characteristics of the froth image are studied by establishing reasonable mathematical models for image distribution feature description, which effectively solve the problems of accurate extraction of the forth surface color, bubble size and bubble surface texture. The extracted froth image statistics are successfully applied in the froth state classification and the intelligent production condition recognition in the mineral flotation process. The main researches and contributions are as follows:(1) A spatio-temporal information fused froth image denoising method based on multi-scale geometric transformation of the froth image sequences is proposed, aiming at solving the problem of serious noises on the froth image, which may lead to inaccurate extraction of the froth surface features. Firstly, a great many of froth images are collected for statistically modeling the froth image coefficients in the multi-scale transform domain. Then, the recovery of the clean froth image signals is obtained by Bayesian least square estimation based on the intra-frame information with the established statistical model of the froth images as the prior knowledge. At last, by weighting the inter-frame recovery signal, it restores the uncontaminated image signal optimally based on temporal and spatial fused image sequence information. It solves the problem of the confusion of the image details and image noises, which is frequently existed in the commonly used image denoising methods. This method can remove the image noise while effectively keeps the edge curves and surface textures of froth image simultaneously, which provides high-quality image signals for the subsequent froth feature extraction.(2) An adaptive froth image color correction method is presented based on the statistical distribution of the spatial structures of the images, since the froth images are prone to have the color cast. The correlationship of the edge response distribution features of the froth images and the corresponding optimal illumination estimation method is analysed in advance, which results in the establishment of the optimal scence illumination estimation model based on the statistical distribution of the edge responses of the froth images. Then, the forh image color will be corrected to the comparative color space under a canonical illumination according to the estimated results of the incident illumination color. During the illumination estimation, a well known image database for color constancy research including11346images with the known illumination built by Ciurea and Funt is used as the training samples to establish the distribution model of the image edge responses. After building the Mixture of Gasussian classification model based on the relation of the statistical distribution features of the images with their corresponding optimal illumination estimation method, it achieves the automatic selection of the optimal illumination color estimation methods. The experimental results demonstrate that this method is capable of achieving the optimal estimation of the illumination of the image and effectively correcting the froth image color, which paves the way of accurate color feature extraction of froth images and automatic identification of the flotation production conditions.(3) An intelligent recognition method of the health states of the reagents operation is presented based on the adaptive learning of the dynamic distribution features of the froth bubble size distribution according to the fact of the bubble size distribution of flotation froth varies with the dynamic changes of the reagent operation, for lack of the effective method to monitor and assess the health states of the reagent operation. Firstly, an improved froth image segmentation algorithm is proposed based on the local pixel gray-level distribution of the froth images in combination with geometric characteristics of the bubble edge curves. It solves the problem of image over-segmentation resulting from the dispersed highlights caused by the light reflections of the mineral particles on the bubble surface. Then, the cumulative density functions (CDF) of the froth images are fitted through the statistics of the segmented region area with the kernel density estimation. The statistical distribution feature sets of the bubble size under the typical operational conditions are learned by unsupervised furthest neighbor clustering (FNC); subsequently, the current health state of the flotation process in the test time period is inferred by the Bayesian rules according to the dynamic change of the bubble size distribution; what is more, the bubble size distribution features extracting under the typical dosage addition conditions are updated timely according to the disturbance of the operation conditions. The proposed health state recognition method can track the dynamic change of the bubble size distribution and achieve the automatic recognition and evaluation of the reagent operation effectively, which lays a foundation for the realization of the optimal control of reagent addition in the flotation process operation.(4) A kind of multi-scale and multi-orientation texture features of froth images based automatic operational condition classification and recognition method is proposed for the ultimate purpose of automatic identification and evaluation of the flotation production conditions according to the subtle change of the forth surface texture. The Gabor wavelet transformation is used to decompose the froth image into in advance in view of the two dimensional Gabor basis function can simulate the responses of the simple cells in the visual cortex of the most mammal brains. The convolution images including the real part (RGFR), imaginary part (IGFR), amplitude part (AGFR) and phase part of the Gabor filter responses (PGFR) are statistically analyzed respectively, whose marginal distribution and joint distribution features are both taken into account in this work. And then the distribution profiles of RGFR (IGFR) and AGFR are characterized by t Location-Scale and Gamma distribution respectively and the cumulative distribution features of each joint distribution of the Gabor filter responses are also calculated. Both the marginal distribution feature parameters and the joint distribution features of the Gabor filter responses at each sub-band are taken into account to construct the froth image texture feature parameter vector. At last, the extracted froth image texture variables are used to unsupervised clustering analysis and supervised recognition of the industrial production states. The experiment results demonstrated that this method can extract the distinctive froth texture in various flotation states and achieve high recognition rates of the flotation production states by these froth image texture parameters.(5) The proposed image statistic analysis methods are applied in a bauxite flotation plant in the Zhongzhou branch of China limited aluminum corporation, where the flotation froth image acquisition device is designed and mounted by deploying the corresponding software system for image processing and process monitoring. The visual features of the froth image are extracted, including froth color, bubble size with size distribution and the froth surface texture, and so on. The optimal texture feature interval of the forth image is obtained in accordance with the relation of the froth image features and the production conditions. Consequently, the vision system can offer the real-time measurement and objective evaluation of the flotation process. The application results indicate that the extracted froth feature curves can offer operator useful production information with operation adjustment advice, which avoid the blindness of manual operation of the flotation process. To summarize, the established system with the proposed method improves the flotation performance efficiency and lays a foundation for the optimal control of the flotation process. There are89figures,10tables and211references in this dissertation.