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The internal combustion system of coal-fired boiler is a complex nonlinear system with large delay and strong coupling. The most basic requirement for the boiler combustion system is that the combustion flame inside the boiler is stable and uniform, and the flame temperature is the most important combustion characteristic of the combustion flame, so the method of using flame temperature as an intermediate variable to control the boiler system has been proposed by many scholars. Compared with the traditional control method that takes the main steam pressure and other parameters as the control variable, this method greatly reduces the delay of the system, and at the same time improves the stability and economy of the system. In this paper, a temperature field reconstruction method based on the improved two-color temperature measurement method is proposed. In this method, a mathematical model between the gray value of flame image and the parameters to be solved in the improved two-color temperature measurement method is established by using BP neural network. Then, the temperature field in furnace is reconstructed according to the gray value of flame image. This method solves the problem that traditional furnace temperature field reconstruction algorithm can not obtain accurate radiation energy data, which makes the measurement and calculation of this method simplified, and the accuracy is improved. The simulation results show that the maximum temperature error of this method is 4% and the average error is around 0.68%, which can meet the requirements of industry.

Reconstruction of Temperature Field in Coal-fired Boiler Based on Limited Flame Image Information