Pulverized Coal Boiler Temperature Field Of Intelligent Monitoring And Diagnostic System

With the increasing-demand of the society for energy source and the technology progressing, diversified coal boiler with big capacity and high parameters has been using in industry. Monitoring the operating-condition of a boiler's combustion system automatically, continuously and accurately, and diagnosing the abnormal state in time, as well as making suggestions to operation, has a very important significance to improve boilers' economy and security.In this paper, firstly studying the temperature distributing status in a boiler's hearth, confirming a normal operating condition on the base of actual measure operating conditions, adopting statistic soft—SPSS11.5 to design the normal operating conditions by the orthogonal test way and then obtaining multi-combustion operating conditions. Secondly, all the operating conditions simulated numerically by adopting the commercial soft CFX4.3 and then the temperature distributing data on the characteristic section in hearth were obtained, so using the data as sample to train and test the integrated adaptive fuzzy-neuron network, which is the base of the dynamic displaying 2-dimension temperature field model between the combustion system input-parameters and the temperature data. The testing result indicates that this model can be used to display the distributing temperature dynamically with higher precision, and satisfies the on-line and veracity commend of the diagnosis system for boiler's running. Finally, the intelligence monitoring and diagnosis system of the temperature-field in hearth was built on the base of the model, which can monitor and diagnose all the wind-coal parameters online and display the temperature distributing in the hearth dynamically and estimate the position of the deflective burning cut-circle.The main work and innovations in this paper have reference significance to how to use the modern artificial intelligence technology and the numerical calculation technology to enhance the supervising and controlling quality of big par-installations. The research results not only have theory value, but also can be used as the technology bases in practice.