Monitor And Optimize Ball Mill'S Coal Load Based On Vibration Singal'S Spectrum Analyses

In accordance with the development of electric power industry, power stations are facing drastic competition to make more profit. Pulverizing system is an important subsystem which used too much station-service power supplies. So it can work out much more potentialities in energy conservation and operation optimization.Pulverizing system has two main problems. One is the high pulverization power cost and low auto control rate, owes to the lack of effective measurement of coal load in ball mill. The other one is nonstandard operation and maintenance of steel balls in the ball mill. There is no method to diagnose the heaped load and diametric proportion of steel balls after the mill run, so the operation and optimization have no bases. Without the right supervision the mill become a low efficiency and high cost system.In order to solve the two problems, spectrum analyses, a modern digital signal technology, is to be used in the coal load measurement and steel balls diagnoses. The new method settled the key problem of pulverizing system's autocontrol and optimization. The main contents of this dissertation are as follows: Analyzed the physical model of coal load in the mill according to the operating principle and the materials moving in the mill. Demonstrated the measurement theory of the coal load based on the mill's bearings vibration. Used the FFT, a spectrum method, to analyses the vibration signals. All above lay a foundation of the next research and examination work.Got the abundant data from the finished ball mill examination. The spectrum analyses' results of the vibration accord with the real work conditions which proved the measurement theory and method of coal load is correct. Analyzed the mill's response to the load step and judged the accident operating mode.Formulated the diagnosis theory of the steel balls' condition in the working mill. Got two useful spectrum information, center frequency and percentages of vibration power, which are used to diagnose steel balls' abrasion and the real-time ball-diameter mixture ratio. Represented the steel balls optimized decision-making based on the diagnosis.Monitored and displayed the coal load according to the mills' bearings vibration power and the DCS data which helped the mill load control easier. Represented the coal load optimize decision-making and designed the mill's autocontrol scheme about load, input entry negative pressure, and exit temperature.Analyzed the bin feeder and direct pulverizing system pulverizing systems' optimization need of coal load, steel balls and ventilation quantity. Represented the optimization decision-making rules of coal load and steel balls. Designed the mill's optimized scheme based on neural networks.Based on abundance data acquisition of field experiments, monitored the mill's coal load and diagnosed the steel balls conditions by using the modern digital signal processing method named FFT, which filled up the technology blank. Solved the key problem of pulverizing system's automation, operation-optimization, and energy conservation and as well as cost reduction. All the achievements have practical value.