Research On Modeling And Optimization Control For Continuous Grinding Process Of Diasporic Bauxite

Beneficiation of diasporic bauxite, created firstly in China, is one of the new 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, thus, the utilization of bauxite is improved. Grinding and classification process is a very important part of the beneficiation process, which grinds the comminuted ores to small particles and liberates the alumina from silica minerals for classification and then floatation. The economical and technical indices of the benification plant are directly influenced by the operation condition of grinding process. The optimization control of grinding process is the most direct and effective method to improve the economic benefits of concentrator.Diasporic bauxite come from many mine resources, the hardness and grade of the ores varies frequently. All of these result in large fluctuation of the grinding circuit of bauxite, low efficiency and high energy consumption. Build unit equipment models, realize the optimization control of grinding process are very important for stable slurry concentration and fineness, reduce energy consumption and improve overall economic efficiency. However, the grinding process is a typical complex industrial process, influenced by many factors, and exist strong coupling. Meanwhile, time-varying of steel-ball loading, particle size distribution of mill feed ore and the nature of the ore make it even more difficult to model and control the process.Based on the in-depth research of the bauxite grinding process, this paper centers the problems of ball-mill product particle size distribution predictive model and process optimization control. To solve the problem of the mill product fineness difficult on-line detection, a particle size distribution prediction modeling method based on laboratory batch grinding experiments was presented; for the characteristics of milling process of bauxite, a multi-objective, multi-model predictive control scheme was proposed; for frequent fluctuations of overflow concentration and fineness, a zone-control method of overflow was proposed; an overall economic optimization objective and a hierarchical optimization control scheme was proposed to calculate the optimal targets setting ranges of control variables which realize the economic optimal operation of grinding-classification process,The main research work and innovative achievements are listed as follows:(1) As an attempt to solve the problem of mill product fineness difficult on-line detection, a particle size distribution prediction modeling method of mill product based on laboratory batch grinding experiments and size-mass balance for the continuous grinding process of bauxite was presented. The value matrix of breakage distribution represents the material property was determined by laboratory wet grinding; a time nonlinear breakage-rate model was established to describe the non-first order breakage of bauxite, different parameters of breakage-rate model were determined by laboratory grinding data and industrial grinding conditions respectively; consider the differences in residence time of particles with different size-intervals, a residence time distribution probability density function was proposed based on flow-pattern classification; finally a particle size distribution predictive model for industrial ball mill of diasporic bauxite was established. The industrial test data verification results show that the accuracy of the model meets the needs of actual production.(2) As an attempt to solve the particle size distribution of first-sand-return difficult on-line detection, a process simulation method for grinding process of diaporic bauxite was proposed. This method based on sequential modular approach, using iterative optimization method to predict the particle size distribution of first-sand-return by field conditions and unit model of ball-mill and classifier. The field test results show that the prediction accuracy of this method meets the needs of actual production, overcome the shortcomings of solids-flow-balance method, which is highly sensitive to the accuracy of ball mill model.(3) For continuous grinding process of bauxite with bauxite ores come from many mine resources and the grade of the ores varies frequently, considering how to decrease power consumption of ball-mill, a multi-objective multi-model predictive control method was proposed. This method firstly build state space concentration predictive model and weighted multi-model fineness predictive model. Then a multi-objective optimization predictive control method is constructed which includes zone control of the concentration and fineness of ball-mill production and economic optimization. At last multiplier penalty function method was used to solve the local optimal control law of the controller.(4) To realize the zone-control of overflow concentration and fineness of spiral classifier, an unequal zone-control algorithm on the basis of MPC theory was proposed, different control strength will be implemented when control variables deviate from upper or lower limit of quality index zone, no control action will be implemented when control variables within quality index zone. In this way, the stability control of the spiral classifier is maximally guaranteed as well as control variables quickly returning the control-zone.(5) In order to achieve the economic optimal operation of continuous grinding-classification process, an overall economic optimization objective and a hierarchical optimization control scheme was proposed. This scheme is composed of two parts:steady-state targets setting and dynamic set point tracking, in each control cycle, the optimal targets setting ranges of control variables will be calculated in the targets setting layer which maximize the economic optimization objective, then the stable tracking of the optimal setting ranges is realized in the dynamic control layer. Database of field adjustment based simulation results show that the proposed scheme can improve the economic benefit of grinding process.