Dynamic Features Extraction And Its Industrial Application Of Mineral Flotation Froth Image Sequences

Mineral beneficiation is generally an indispensable industrial process in the utilization of the most natural mineral resources. Among the commonly used mineral dressing methods, froth flotation is the most important technology and almost all of the different ores can be separated by this method. Flotation process is a complex continuous physicochemical process which occurres in solid-liquid, solid-air and liquid-air three-phase interface. In the flotation process, froth layer is the key factor to production performance. Currently, the flotation operation is mainly adjusted through observing the upper surface of the froth by the experienced operators. The naked-eye observation based flotation process operation fashion is strongly subjective and inefficient. It is deficient in qualitative descriptions of flotation froth states and the objective evaluation of the production performance, which consequently results in frequent fluctuations of production indices and low recovery of raw materials with large consumption of reagent dosage. Since1990s, some developed countries have introduced the machine vision to the flotation process monitoring and control, which incurs increasing interest of the researching communities and plant operators. It is significant to research machine vision based flotation process monitoring, modeling and automatic control method and technique in theory and practical application to improve the efficiency of the usage of the mineral resource and realize the sustainable development of enterprises.It’s essential to extract the accurate visual characteristic parameters of the froth surface to realize the machine vision based automatic control of the flotation process. There are two kinds of visual characteristics of froth surface appearance. One is based on the froth characteristics of single frame image, called static parameters such as bubble size, bubble shape, surface froth color and froth texture characteristic and so on. The other is the characteristic based on image sequence known as dynamic parameters, generally including the bubble velocity, bubble stability and so on. Currently, researchers both at home and abroad mainly focus on extracting and analyzing the static parameters while little attention has been paid to dynamic parameter extraction. It’s worth noticing that the dynamic parameters are also indispensable to realize the objective evaluation of the flotation production statuses. The inherent relation between the flotation production conditions and both the static and dynamic characteristics of froth image are presented in this dissertation based on the analysis of the flotation mechanism. This work mainly focuses on researching the dynamic parameter extraction and characterization method of flotation froth based on digital image processing with the industrial application to a bauxite flotation process monitoring and control system. The main contributions are as follows.(1) A Bayesian denoising method integrated spatial-temporal image information is proposed based on multi-scale geometric analysis, aiming at solving the problem of inaccurate feature extraction of the froth images with serious noise contamination. Curvelet transformation is adopted in advance to express the geometric structures and surface texture of the surface froth image sparsely. Then, the marginal statistical distribution and the joint statistical distribution model of the Curvelet transform coefficients of single-frame image and multi-frame images is constructed after analyzing the statistical characteristic of the image coefficients in the transform domain. According to a specific cost measure, Bayesian inference is used to estimate the ideal uncontaminated coefficients in the transform domain. At last, the inverse Curvelet transformation is applied to get the denoised froth image. Compared to the other image denoising algorithms, this method can achieve higher peak signal noise ratio (PSNR) and keep the froth image details effectively while making noise reduction, which provides high-quality processing signals for the subsequent feature extraction of froth images.(2) An adaptive macroblock tracking method of froth image based on the combination of Fourier-Mellin transformation and gray template matching is proposed to measure the accurate velocity of the flotation froth flowing to the scrapper through automatically tracking the deformed bubbles in the flotation cells. Since the mineralized bubbles in the flotation cells subject to indispensable geometric distortion in scales and orientations, the froth flow velocity cannot be extracted accurately by the traditional object tracking methods, which cannot tracking the non-rigid froth bubbles with serious geometric distortion in the flotation process. This method aims to make a complement of the traditional object tracking methods to measure the froth velocity accurately. The sub-blocks are located and assigned adaptively in advance by scanning the highlights of the froth image automatically and estimating the bubble sizes effectively. The deformation coefficients and the approximate displacement of the sub-blocks in the adjacent image frames are computed by Fourier-Mellin transform, which are used to obtain the non-deformed sub-blocks by inverse geometric transformation. Then, grey template matching is adopted to search the positions of the non-deformed sub-blocks in the local neighbourhood around the first estimated displacement to measure the precise displacement of the sub-blocks in the adjacent froth sequences. This method solves the problem of the froth velocity characteristic cannot be extracted accurately with serious geometric distorted flotation bubbles.(3) A new froth velocity characteristic extraction method based on image gray SIFT(Scale Invariant Feature Transform)with Kalman filtering is proposed. Since the flotation bubbles collapse and burst seriously in the flotation process, the commonly used motion estimation methods cannot match and track the collapsed bubbles areas effectively, which result in inaccurate flow velocity measurement. Firstly, Kalman filter is used to predict the approximate displacement of the sub-blocks in the adjacent frames, then the SIFT features are extracted to registered the sub-block in a narrow neighbor area around the approximate displacement. This method greatly reduces the aimlessness of SIFT features extraction and feature points matching, which greatly improves the efficiency and the accuracy of feature point matching. It computes conveniently with low computational cost and successfully solves the problem of measurement of the froth flow velocity with serious bubble burst and collapse, which is convenient to apply to the real industrial flotation process. (4) A method to extract the deformation coefficient and bursting rate features of the flotation froth is presented based on digital image processing aiming at solving the problem of a qualitative description of the froth stability feature. Based on the froth flow velocity, the corresponding stability evaluation criteria are defined, which results in an effective qualitative description of the deformation coefficient and burst rate of flotation froth. This method is able to extract the froth stability in real time, which provides the digital parameters of the froth images to evaluate the flotation production statuses objectively.(5) A flotation froth image acquisition and processing flat is designed and established in the bauxite flotation plant of Zhongzhou branch of China Aluminum Company with the corresponding visual monitoring system. It extracts the visual characteristic of the froth surface appearance timely and analyses the preliminary relation between the flotation production conditions and the froth velocity characteristics, which results in online measurement of the froth surface and objective evaluation of the flotation production statuses. The froth feature curve can afford clear indication information of the production conditions and provide guidelines for flotation operation, which avoid the blindness of traditional naked-eye observation based flotation operating. The monitoring system improves the flotation efficiency greatly and lays a foundation for the optimal control of the flotation process.