The Applied Research To Realize Closed Loop Control System For Optimizing Boiler Combustion

The power station boiler is one of the three main frames of thermal power stations. Boiler is quite complex and huge in size, and the process happens in it is multi-variables, and the parameters are heavily coupled together. So its control and optimization are targeted as one of the most key problems by researchers. The boilers combustion process is selected for the studying object in the paper, and a combustion optimizion model for the boiler combustion system is set up by use of RBF neural network. In the paper, the boiler load, the second air quantity, the OFA quantity and so on are selected for the input parameters, and the the second air opening and so on are selected for the output parameters. The boiler system is modeled through its historical data in the boiler combustion process. In this paper, RBF Neural Network is selected as a tool for modeling according to the characteristics of boiler systems. So it enhances the generalizing ability of RBF Neural Networks and improves its applicability.The parameters which are in their best condition have close relationship with heat efficiency and NOx emission of power station boilers, and this is the criteria of optimal target. The boiler combustion system is modeled through corresponding algorithm. Historical data based on the model which is set up in order to find the best setting value of them. The setting value is provided for DCS in order to instruct the combustion optimizion, and the model reflects the dynamic characteristic of the boiler combustion. The studying is very good for the theory directing of the boiler designing and optimization.In the neural network model closed-loop realization preliminary probe into the analysis, this paper summarizes the realization of the insufficiency of the and difficult.