As the ultra-low emission requirement for thermal power units in China is that the concentration of sulfur dioxide emissions should be less than 35 mg/m3,accurate prediction and control of the SO2 emission concentration is of great significance for the environmental protection operation of thermal power units.In order to deal with the prediction of SO2 emission concentration in circulating fluidized beds,a prediction model of SO2 emission concentration based on deep belief network(DBN)is established by introducing deep machine learning method.Firstly,the operational variables affecting the concentration of SO2 emissions are determined as model inputs through mechanism analysis;secondly,the DBN network is used to extract the deep features of the model inputs,and ELM is used as the regressor to establish the prediction model;finally,the DBN-ELM model is compared with three prediction models of SO2 emission concentration.The results show that the root-mean-square deviation and average absolute error of the model are 175.3 mg/m3 and 117.6 mg/m3 respectively.This prediction accuracy is much higher than that of the other three comparison models.And it has more application value in practical engineering.
关键词
深度信念网络/SO2排放浓度/预测模型/极限学习机
Key words
deep belief network/SO2 emission concentration/prediction model/extreme learning machine
万方数据