Study On Combustion Stability Of Pulverized Coal Boiler

Electric energy is indispensable to human life. Although new energy power generation has developed rapidly in recent years, coal-fired power generation still accounts for most parts of the overall capacity. Pulverized coal furnace is one of the most important equipment in coal-fired power generation, its combustion stability directly related to fire safety and economy of power plant. In this paper, lots of ways based on the classic BP neural network and improved particle swarm method combined with BP neural network have been used to study the combustion stability of different pulverized coal furnace.In this paper, the research content is embodied in the following aspects:1. This paper introduces the BP neural network model, the advantages and disadvantages are detailed described. The furnace combustion stability of the neural network model is established. When choosing a number of hidden layer nodes, the original golden section method is improved. With the rest of the number of hidden layer nodes, comparing the calculation results show that this method has the advantages of low computational cost, high accuracy.2. Aiming at the shortcomings of the BP neural network, this paper USES the particle swarm optimization algorithm for the weight value and threshold of BP neural network model for optimization. And at the same time to join in the classic particle swarm algorithm of extremum disturbance and variation of two ideas, to optimize the combustion stability of model parameters, and a simulation was carried out.