1.Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China;
2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, Hebei 066004, China
Abstract：In order to accurately predict the NOx emissions of circulating fluidized bed boiler,a GSA-SVM model based on support vector machine (SVM) of NOx emission was established on the base of the sample data of circulating fluidized bed boilers in a power plant.The gravitation search algorithm (GSA) was applied to the process of the model parameters optimization because the accuracy and generalization ability of SVM model depended on the parameters.The prediction performance of the model was tested by sample datas under different experimental conditions.The regression model was compared with BP and the SVM model whose parameters were optomizated by particle swarm optimization(PSO) and genetic algorithms(GA). Finally the simulation result shows that the GSA-SVM model has a good recognition ability and generalization ability
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2013, Vol. 34 
