Real-time Monitoring Of Unburned Carbon Content In Fly Ash Of Coal-fired Utility Boiler And Optimization And Application Of Boiler Combustion

With the rapid development of modern economy,energy saving and emission reduction have aroused wide concerns.For coal-fired thermal power plants,it is of great significance to further improve the combustion state of boilers and reduce operating costs.Online monitoring of the unburned carbon in fly ash can timely reflect the current combustion state of boilers,and it is easy to judge whether boiler combustion needs to be optimized.The optimization of boiler combustion takes the advantages of low cost,convenient operation and high controllability,meaning that the research on on-line monitoring of unburned carbon in fly ash and optimization of boiler combustion are significance.Boiler combustion is a very complex process with strong coupling,and the present study aims to ensure that the boiler can reach the optimal combustion state under different burning conditions.The unburned carbon in fly ash is one of the important indexes of boiler combustion state.This thesis provided an accurate optimization signal by monitoring the unburned carbon in fly ash online,and then optimized the boiler combustion process by using grey wolf optimizer algorithm.This thesis proved two methods to monitor the unburned carbon in fly ash online.Firstly,the influence of operating parameters on the carbon content of fly ash was studied and analyzed.Based on a large number of historical boiler combustion data,BP neural network was used to establish the prediction model of unburned carbon in fly ash.Then some data were selected to test the accuracy of the prediction model.Secondly,based on the combination of online burn-out and gas detection,an online monitoring system for unburned carbon in fly ash was designed to realize online monitoring of carbon content in boiler fly ash.This thesis used a new swarm intelligence algorithm——grey wolf optimization(GWO)to optimize the boiler combustion operation parameters to reduce unburned carbon in fly ash and improve thermal efficiency.The grey wolf optimizer is compared with other optimization algorithms in optimization performance and optimization time,and the stability of grey wolf optimizer in boiler combustion optimization process is verified.The simulation results show that the grey wolf optimizer has great advantages in optimization accuracy,convergence speed and stability.The ANN-GWO method proposed in this thesis greatly reduce unburned carbon in fly ash and improve thermal efficiency.The simulation results show that the optimization running time is significantly shortened and the algorithm is easy to implement.It is suitable for online optimization of boiler combustion process and provides a new effective method for coal-fired utility boiler.