| Although there are plenty of hematite resources in China, they are all difficult to be separated for the nature of low grade, weak magnetic, disseminated, complex mineral composition. Therefore, the roasting and magnetic separation technology is employed. Hematite beneficiation process consisting of a shaft furnace roasting unit, grinding unit and magnetic separation unit will separate the useful mineral and gangue and enrich the useful mineral composition, and thus produces the overall concentrate with qualified grade and tailings. Optimal operational control of a benefication process is to generate the setpoints of control systems of each unit according to their operational indices such as magnetic tube recovery rate, particle size, concentrate and tailings grade, etc. The control systems force the controlled variables to follow up the setpoints so as to control the operational indices into their targeted ranges, at the same time, improving the magnetic tube recovery rate of shaft furnace, particle size and concentrate grade as high as possible, decreasing the tailings grade as low as possible. In fact, the above targeted ranges of operational indices are determined by the production indices, i.e. overall concentrate grade and output, of the beneficiation process. Therefore, the optimal decision-making of operational indices is of great significant for improving the overall concentrate grade and output.The objective of decision-making of the operational indices is to make the overall concentrate grade and output into their targeted ranges and improve them as high as possible. The relationship between the operational indices and the production indices not only relates to the process mechanism, but also relates to the specific process technical factors. This fact leads to mechanism of the relationship is unclear. As a result, it is difficult to be described using exact mechanism model. The dynamic uncertainties, in terms of the external environment and internal disturbance of the benefication process, lead to frequent variations in the operational indices decision-making problem formulation in terms of the targeted ranges and limitations of production indices (overall concentrate grade and output), and the limitations of constraints (i.e. composition variation of raw ore, run time fluctuation and maximum processing capacity of grinding unit, etc.). Therefore, optimal decision-making of operational indices is a multi-objective nonlinear dynamic optimization problem for a process difficult to be modeled and it is difficult to solve using the existing optimization approaches. In practice, the decision-making of operational indices is usually carries out by the technical engineers with experience. As the improper or untimely manual adjustments often can not guarantee the production indices into their targeted ranges, it is difficult to achieve the optimal operation of the whole process, resulting in poor product quality, high energy consumption and resource consumption and other issues. Therefore, it has important theoretical significance and application value to carry out the research on how to realize the timely and effectively optimal decision-making of operational indices under dynamic environment in order to achieve global optimization of the whole production process.Subject to the above problem, suppoted by the973projects "the total control strategy and operational control approach for complex manufacturing processes" and the National Natural Science Foundation project "closed-loop optimal decision-making approach of technology index for complex industrial processes under dynamic environment", the research on the optimal decision-making of operational indices for benefication processes has been carried out. The detailed work has been summarized as follows:1) The mathematical formulation of the decision-making of operational indices for beneficiation process is presented. In this formulation, the performance is described taking the operational indices (the magnetic tube recovery rate, particle size of high-and low-intensity grinding unit, concentrate and tailings grade of high and low intensity magnetic separation unit) and the measurable disturbance such as the nature of raw ore and working condition (such as raw ore grade, capacity per hour and run time of high-and low-intensity grinding unit, and grade of waste ore, etc.) as the inputs, and the production indices (the overall concentrate grade and output) as the outputs. The decision variables are the seven operational indices. The constraints include upper and lower limitations of the overall concentrate grade and output and the operational indices, the lower limitation of the maximum processing capacity and the raw ore grade etc. and the equality constraints, i.e. the relationship between the operational indices and the production indices. The objective of optimal decision-making of operational indices is to make the overall concentrate grade and output into their targeted ranges and as high as possible. Moreover, the difficulties in solving the above problem using the existing optimization methods are analyzed.2) Subject to the problems that multi-objective optimal decision-making of operational indices of benification process, the relationship between the operational indices and the production indices difficult to be modeled using exact mechanism model, and variations on the objective targets and range and constraints, a structure of optimal decision-making is proposed combining the optimization, the prediction of production indices and the dynamic tuning of operational indices. The structure consists of four modules, namely optimization of initial value of operational indices, predictive model of production indices (overall concentrate grade and output), priori-and posteriori-evaluation of production indices and dynamic turning of operational indices. At the same time, combining multi-objective evolutionary algorithm and case-based reasoning, process operation data and expert knowledge, dynamic tuning and rule extraction, design approaches of the above four modules are proposed.3) The hybrid modeling strategy of overall concentrate grade and output is proposed which is composed of a linear model and a nonlinear error compensation model. The least-squares support vector machine (LS-SVM) is adopted to establish the nonlinear model. Subject to the problem that the mean and variance are unsuitable in model parameter estimation of processes with non Gaussian disturbance, the idea of probability density function (PDF) control is introduced into the performance of parameter selection problem. It is to turn model parameters so that the modeling error PDF is controlled to follow a target PDF to guarantee the model accuracy. The predictive model of overall concentrate grade is established adopting the above approach, where the inputs are the operational indices. Moreover, considering the effect of different production conditions on the concentrate output, the multi-model based prediction model of overall concentrate output is proposed. Finally, the simulation experiments are carried out using the process data collecting from real plant and the results show the effectiveness of the proposed approach.4) The dynamic tuning approach of operational indices based on rough set rule extraction is proposed. First, the form of increasement rules is constructed between the production indices and operational indices according to the characteristic of collected data. Then, the rules are mined from a large number operational data using rough set rule extraction. When the error between the actual/predictive value and the targeted value of overall concentrate grade and output occur, the mined rules are performed to compensate the operational indices. The simulation experiment of feedback compensation is carried out and the results show the effectiveness of the proposed approach.5) The experiment research of the proposed optimal decision-making of operational indices is carried out on the semi-physical simulation platform in our laboratory. Adopting the proposed approach, three experiments are carried out. These experiments validate that under the variation condition of raw ore grade, run time of grinding unit causing by fault and processing capacity of grinding unit, and at the same time the targets and ranges of overall concentrate grade and output are changing, the proposed system can produce the operational indices in time and make the production indices into their targeted ranges. Comparing with the manual adjustment, the proposed approach can force the magnetic tube recovery rate improved by2%; particle size of high-and low-intensity grinding unit increased by1.49%and1.98%, respectively; concentrate grade of high-and low-intensity magnetic separation unit raised by0.57%and1.26%, respectively and tailings decreased by0.31%and0.67%, respectively. Finally, the overall concentrate grade and output are enhanced by0.57%and132.37t/d, respectively, which means that the optimization of whole production line is realized. |
PDF Full Text Request