Study Of The Circulating Fluidized Bed Boiler Combustion Optimization Based On The Improved Wind Driven Optimization Algorithm

With the rapid development of economy,the demand for electricity increases sharply.However,thermal power is the main form of power generation in China,which must lead to two major problems: energy consumption and environmental pollution.Therefore,thermal power enterprises face with the dual tasks of energy saving and emission reduction.The research on power plant boiler combustion optimization has become a significant research topic both at home and abroad,and scholars have achieved some results.In order to improve the combustion efficiency and reduce pollutant emissions of the boiler in power plant,this paper take the 300 MW subcritical circulating fluidized bed boiler as an object of study and establish its model based on Extreme Learning Machine(ELM),because of the complexity of the boiler combustion system and the limitations of traditional modeling methods.The Adaptive Wind Driven Optimization(AWDO)is applied to the process of the model parameters optimization to get the optimal model because the accuracy and generalization ability of the model depend on the weights and threshold values.Finally,according to actual condition to determine adjustable parameters and the range of optimization,and then AWDO algorithm is used to find the best parameter combination for the selected samples.Moreover,by adjusting the ratio of the weight,the optimization effect can meet the running demand of different power plants.The simulation results show that the established models using ELM and AWDO have high prediction accuracy and generalization ability.The effect of optimization is very satisfactory.The system of boiler combustion optimization in this paper can achieve aims of high-efficiency and low-pollution emission.