Froth Image Based Soft Sensor Of PH For Bauxite Flotation Process And Its Application

Flotation is an important part of alumina production process using 'bayer process with dressing'. Slurry pH is a direct reflection of the production condition and the consumption of sodium carbonate. However the pulp with poor mobility, high temperature and high corrosion, easy to plug the contact pH test device, while off-line tests are adopted by most of concentrators, which leads to a long lag time, and is difficult to continuously detect. Studies have shown that froth image is the indictor of pH, so researching real-time detection of pH values using froth images is great theoretical significance and practical application value.On the basis of the analysis of bauxite flotation process mechanism, considering the correlation of the bubble images and the pH value of pulp, froth color, size, shape and texture features are extracted, and the soft sensor of pH is established made up of multiple sub-model using image features, which is applied on froth flotation industry successfully. The thesis research and innovation results are as follows:(1) For the complex reaction mechanism of the pH value neutralization process of bauxite flotation process, the importance and the significant impact of pH on flotation performance is discussed from three aspects of bauxite particles floating, the activity of flotation reagent and ion composition. According to the law of conservation of mass and charge balance from the macro and micro aspects, it is proved that pH is the basis of adjustment of sodium carbonate and there is a typical non-linear characteristic in pH neutralization process based on ideal mixed continuous stirred tank reactor. A number of froth images are analyzed to choose proper image features, which shows that froth image is an indicator of pH.(2) Considering the difficulty to extracting froth features for bauxite flotation process, indicative function to pH of froth image, the feature extraction of bubble color, size, shape and texture is researched. As the interference of highlight and equipment aging causes color distortion, an image inpainting method is used to remove the top highlight of the bubbles, and then robust calculation with device-independent of froth color is realized based on color space conversion. Around the mineralized bubbles of various shapes, uneven scene illumination adversely affect the bubble image segmentation, the inertia weight and acceleration factor of particle swarm optimization is set to the function of global optimum fitness to detect threshold of valley edge. The local gray minimal is used to select boundary detection template, and then the froth image is segmented according to logic rules to obtain the bubble shape characteristics. In view of the radial elongated features along the forward direction for the surface froth, while the traditional spatial gray level co-occurrence matrix ignores the directional difference, which leads to inaccurate texture calculation, a multi-angle-fused spatial gray level co-occurrence matrix is proposed to calculate second-order statistics to describe texture. The image features provide plentiful data for modeling of pH soft-sensing.(3) In view of the non-linear characteristics of the pH neutralization process and the characterization to pH of the bubble images, the local soft-sesing model of pH is set up based on multi-kernel least squares support vector regression machine. Multiple kernels with different characteristics are convex combined to improve learning ability and generalization ability of the model. Focusing on the ignorance of cost and computational complexity of multiple kernel learning, a novel cost constraint multi-kernel learing idea is raised. The original optimization problem of multi-kernel least squares support vector regression machine is converted into the form of a second order cone programming, and then a cost factor of mult-kernel learing is difined to to determine the optimal combination of kernel functions. To reduce the computational complexity, an improved nearest neighbor peaks clustering algorithm and Schmidt orthogonalization method are adopted. The total cost of multi-kernel learing is evaluated according to the number of support vectors and active kernel, which saves the variable storage space and computing time.(4) Aiming at the deficiency of reflecting the entire flotation conditions for the local model, a global modeling using multiple working condition sub-models fuzzy combination is achieved. The flotation process is divided into three working conditions based on feature weighted fuzzy multi-classification support vector machine, and sub-model of bubble image features and pH value is established in each range of conditions. Distribute the importance metric of image features according to sensitivity differences in response to operating conditions based on information gain. The fuzzy membership of the bubble image on the working conditions is used as the combination weight of multiple sub-models to realize global soft sensing model. Effective results using industrial data of off-line froth image are achieved on the recognition of flotation performance and the soft-sensing of pH.(5) Taking bauxite flotation process in industrial field as a research case, a process monitoring system based on froth image analysis is developed, which could extract froth features on line and detect the pH values of flotation slurry in real time. That the flotation performance can be evaluated and expert rules can be updated according to froth image and pH achieved by soft-sensor, provides operational guidance to adjust the sodium carbonate amount.