Diagnosis, Analysis And Application Of Combustion Stability And Economy Of Power Plant Boilers

It is a complex and important issue that the combustion stability and economy of power station boilers in the field of thermal energy engineering. While the combustion of pulverized-coal in furnace is a complex physical and chemical reaction, which is occurred under the condition of large space, frequent fluctuation and having conspicuous three- dimension character. Therefore, it is significant to study on effective methods of flame detection, diagnosis and analysis, and to establish reliable computing-models of boiler combustion efficiency to optimize the operation of boilers.In order to monitor and control the state of combustion and analyze its stability at the same time, the essay establish a dynamic model between sets'power and radiant energy signals which reflects the combustion status of whole furnace from flame images derived from the flame detection equipment of the three-dimension temperature field monitoring system built in a 200MW boiler unit. It is found that the radiant energy always fluctuates violently when the boiler's power changes, indicating that the main reason for frequent flame failures is the excessive combustion adjustment. Besides, analyzing the stability and diagnosing the combustion status are carried out by distilling characteristic parameters from the flame images which reflect the whole furnace. According to a record of the whole furnace extinction accident, it has been verified that the method could diagnose the combustion status well.Generally, economical combustion and optimized operation of boilers need accurate model of boiler efficiency, while there are many complex influencing factors during the running period of boilers. As a non-line mapping means, compared with traditional optimizing methods, BP neural networks has much more advantages which can help to deal with this kind of problem. Herein, by carrying out online training BP neural networks using VC to call MatLab, and illuminating the process with specific test data, a nice prediction model for boiler efficiency is obtained. Finally, through verifying check data using trained network the result is found to be satisfactory and based on which, an expert system of selecting coals and optimizing combustion is explored according to a certain power plant. The system has applied to the field of a power plant preliminarily, proving that it could improve the economy of boiler operation to further degree.