Application Of Particle Swarm Optimization Algorithm In Combustion Control For Power Plant Boilers

Thermal power plants burn a lot of coal in the process of generating electricity,the burning of these coal will bring serious pollution problems to environment.Research shows that,more than 60 percent of the nitrogen and oxygen compounds in China each year come from burning boilers in thermal power plants.In addition to causing pollution to the environment,these nitrogen and oxygen compounds are also a greenhouse gas,which will have a great impact on the earth's environment.The input and output parameters of combustion process for the boiler are recorded by testing,researching the interaction between these parameters,it can effectively reduce coal consumption of power generation and reduce emission of nitrogen and oxygen compounds.Therefore,it has great practical significance and application value to carry out research on boiler combustion optimization control in thermal power plants.This paper takes a large tangential coal-fired boiler as an example,RBF neural network algorithm based on particle swarm optimization is used to research optimal control process of boiler combustion.The paper discusses research background and significance,the research status at home and abroad of boiler combustion optimization control are elaborated,giving research methods and main content.On the basis of summarizing and analyzing optimization and adjustment method for boiler combustion in power plant,the difference of boiler combustion consumption,NOx emission control,boiler combustion adjustment and control are analyzed.The basic theory of particle swarm optimization algorithm is discussed,the basic principle and algorithm implementation process of particle swarm optimization are studied.The RBF neural network model based on particle swarm optimization is constructed,17 input variables were taken as optimization variables and 7 output variables as optimization targets.Sample data collected on site were normalized,the normalized sample data were trained in the RBF neural network model,comparing the predicted output variable value of the model with measured value,the results show that established boiler combustion control model reflects well the combustion performance of actual boiler.The optimal control parameters of boiler combustion are solved by particle swarm optimization algorithm,the optimal results of boiler combustion control are obtained,it providesreference for optimal control of boiler combustion.