Intelligent Material Measure And Control Of BBD Coal Mill

BBD Ball Mill is a widely used milling device in power plant system. It has the advantages of low energy consumption, high efficiency, widely range of grinding coal, and so on. In fact, most of the mills are working at the conservative conditions for a long time, which makes the milling system a considerable power consumption rate. A major reason for this is the difficult to accurately measure the material level. In addition, BBD Ball Mill milling system is a nonlinear, large delay, large inertia and strong coupling charged object, it is very difficult to implement optimization control. In order to ensure the material in coal mill barrel at the appropriate level and achieve the mill operation at economic conditions, mill's material level detection and optimal control becomes an urgent problem needed to be solved when using coal mill.According to above problems, firstly, this paper introduces the BBD Ball Mill's basic structure and working principle. Then, the operator characteristic curve of BBD Ball Mill is achieved, through a large number of experiments. At the same time, based on its operating characteristic curve, analyzes the parameters and influencing factors.Based on the mechanism analysis of BBD Ball Mill, intelligent material measure method by fuzzy neural network multi-data fusion is presented in this paper, fuzzes processing by fuzzy rules with the variable parameters of multi-sensor acquisition, constructs neural networks to fusion the data, and then the integration results are the expected material values. The proposed methods can enhance the accuracy of material measure, improve the problems of the low accuracy of type-1 fuzzy neural network multi-data fusion system, and use two-type fuzzy neural network multi-data fusion system to measure the material. By simulation experiments, the results show that, there is a marked rose in measure accuracy through using this improved method, and then a more accurate measurement of material level is obtained.Finally, based on the control system for the milling issue, the two-fuzzy neural network control method is given, in this paper. It can not only avoid limitation of control rules which is difficult to be extracted by manually in the application of fuzzy control, but also simplify the multi-variable fuzzy control system design. Thus, the proposed method has strong application significance.