Modelling Of The Continuous Ball Milling Process Of Bauxite And Key Model Parameters Optimization

Beneficiation of diasporic bauxite, created firstly in China, is one of the new potential technologies to process high silica bauxite, which eliminates some silica minerals to increase the grade of the ores so that they are suitable for the Bayer process, and in consequence, the cost of alumina production is reduced. Ball milling is an important operation unit in the beneficiation process, which grinds the comminuted ores to small particles and liberates the alumina from silica minerals for classification and then flotation. The economical and technical indices of the benification plant are directly influenced by the grinding process.In practice, the ores come from many mine resources and the grade of the ores varies frequently, and the process is controlled empirically by the operators. All of these result in large fluctuation of the grinding circuit and low efficiency. So, modelling the process is significant to understand and to optimally control the process. However, the ball milling process is influenced by many factors, including the properties of the materials, the media characteristics, operating conditions, mill size and speed etc., so that it is a complex process. Meanwhile, time-varying of these factors and other stochastic factors make it even more diffcult to model the process.The breakage distribution function (B), residence time distribution (RTD) of the miaterial in the mill and the specific breakage rate function (S) of the continuous milling process are the key parameters to establish the ball milling model. Therefore, these three functions are studied and optimally determined to establish the population balance model. Finally, data reconciliation of the grinding-classification process based on the ball mill model is studied. The prediction results of the industrial process show the effectiveness of the model. The main contributions are as follows:(1) The size distribution function can not be correctly determined because there are too few medium size particles in the natural size distribution of the bauxite. Meanwhile the mono-sized grinding method is time-consuming and expesive. So, a test method with make-up feed is adopted to get a large amount of test data. The breakage rates of the bauxite are found to be non-first order from the test data. That is, the coarse and the fine size intervals break slowly and follow non-first order, and the medium size intervals break fast and follow the first order. The non-first order is most probably caused by the heterogeneity of the ore.(2) According to the characteristic that breakage rates of different size intervals decrease fast at different rates with time at the beginning and then become to be first order, piecewise linearized method is proposed to describe the non-first order. In the method, grinding time is devided into pieces, and breakage is assumed to be first order in each time piece. Breakage rates and product size distribution are then calculated by taking the product of last piece as feed. So, the 3D relationship of the breakage rates varying with particle size and grinding time is accurately determined and the model solving problem is simplified. The breakage distribution function is then optimally determined by using back-calculation method. Validation results show that relative errors of the cumulative size distribution are all within±5%, and absolute errors of noncumulative size distribution are all within±2%, which means high accuracy of the obtained parameters.(3) Based on the mechanism of the material transportation through the mill and the exit classification of the mill, the two small-one large reactors model is used as the RTD model. Because it is difficult to use the radio tracking method to get the RTD parameters in the bauxite grinding process and fitted RTD will influence the accuracy of breakage rate function, a mean RTD estimation method based on measured and empirical data is then proposed to avoid the influence of RTD on breakage rate.(4) Considering of the heterogenerity of the bauxite, a non-first order breakage rate model is proposed for the continuous grinding process. Due to the uncertainty of the boundary constraints of the breakage rate identification problem, an optimization method based on soft constraints adjusting is proposed, which insures the breakage rates are satisfying and cost of searching is reduced. The optimal breakage rates under different operating conditions are then identified from the industrial data. A soft sensor using least square support vector model (LS_SVM) is proposed to calculate the breakage rates using milling condition parameters.(5) In order to correct the sampling and analysis errors of the process data, a triple-layer data reconciliation model is proposed for the grinding-classification process based on the characteristic of the process data. Meanwhile, the ball mill unit is introducted into the size distribution reconciliation to increase the redundancey the process data. Then, the method of solving the reconciliation problem layer by layer based on PSO is proposed to reduce the time complexity of the problem. The statistical analysis of the reconciliation results and comparison of them with the reconciliation results using a commercial software validate the effectiveness of the reconciliation model and the method, and also the ball mill model.