| Ball mill is the key equipment in the intermediate coal pulverizing system and widely used in thermal power plant. It works with high energy consumption, low efficiency and great noise. Due to complex Working condition, existing control equipment can not meet control requirements of ball mill system. Manual control is even available in some places. In order to improve operation stability and economy, cut down equipment loss, it's necessary to keep ball mill operating in optimum working state. In this paper the control of ball mill load system is studied.Grey system theory is a way to solve uncertain problems with few data and poor information. Ball mill load is a grey system with large time delay and time-varying parameters. It's suitable to control the system with grey prediction control. Therefore, this paper attempts to figure out a simple and practical control method by combining some control methods with grey prediction control. Firstly, due to simple calculation and wide application of PID control, grey PID control method based on grey prediction is improved for ball mill load system. Secondly, single neuron control and neural network PID control are applied in ball mill load control. Then these methods are improved based on grey prediction overcoming their shortcomings. Thirdly, a model free adaptive control is studied and applied in ball mill load. Grey method is firstly introduced into model free adaptive control to improve control result in china. Finally, Model predictive control which is commonly used for control of complex object with large time delay and time-varying parameters is contrasted with the methods based on gery prediction control. The analysis shows that the compound controls based on grey prediction are simple and practical when used in ball mill load system. The compound control based on grey prediction is a potential control solution for the system with large time delay and time-varying parameters.
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