Based On The Audio Signal Of The Mill Optimization Control

Ball mill has the characters of grinding with kinds of coals and controlling simple etc, but also exist widely high consumption of the ball, low degree of automation systems, and poor maintenance efforts. How to optimize milling system of automatic control systems to reduce the milling milling consumption has important practical significance. However the coal pulverizing storage system is a highly relevant, large time delay and MIMO nonlinear system and its dynamic properties change widely with circumstance, Only conventional PID control has been difficult to achieve ideal control effect. This paper presents a systematic solution. For the control system, there are two core technologies need to address,one is Ball mill multi-variable control system identification and decoupling problem(System identification and Decoupling )the other is Ball mill load identification .In this paper, the main job is as follows:1. Measuring the coal load of ball mill is important for carrying out auto control,optimal operation,energy saving and reducing. A method that using audio singal 1/3 octave spectrum identifying coal load is put forward in this paper,which can get the characteristic frequency bands in different coal load,then reflecting the changes in coal load of ball mill and laying the groundwork for the introduction of audio signal ball mill to the coal load control system.2. It is the key to identify effectively and control the material level of ball mill for the pulverizing system realizing optimal operation and energy saving and consumption reduction. The model of radial basis neural network (RBF)based on radial basis neural network (RBF) is built according to the running charactristics of the ball mill to recognize the various working conditions and the recognition level in the operation. The simulation results show that the network works well, which laid a foundation for the optimal operating and automatic control of the milling operation3 The milling system are highly correlated, large time delay, MIMO nonlinear systems, and its dynamic characteristics changes with operating conditions on large scale,so conventional PID control has been difficult to achieve ideal control effect. In this paper the effective control using fuzzy and internal model decoupling control technology for the relevant parameters of milling system ensure the safety of the milling system and highly efficient operation.4 A PlantScape DCS (distributed control system)is introduced and the fuzzy internal model control algorithm of the realization way in the PlantScape DCS system is discussed.Finally,the ball mill automation applications in the DCS is optimized.All of the above-mentioned control strategy used in the milling of the automation control system in He'nan branch, China Aluminium Corp. ensured the milling system work well in safe and efficient operation. In this paper, the research and development of advanced control systems realize automatic control of the various parameters of the milling system, and the results of their research is the leading level at China and abroad.