Abstract
Accurately predicting NOx emissions during boiler combustion is of significance for the operation and control of the boiler combustion systems in coal-fired power plants. According to the characteristics of the boiler combustion process with strong disturbances, highly nonlinear and multivariate coupling, a fusion model based on mutual information variable selection and Hyperopt optimized GRU neural network (MI-HGRU) is proposed. The method can accurately select the parameters of boiler combustion process base on mutual information feature selection algorithm. A GRU neural network prediction model with Hyperopt optimization is established to achieve accurate prediction of NOx emission during boiler combustion. The modeling experiment was using a NOx emission dataset from a power plant boiler in Guangdong. The experimental results show that the MI-HGRU method has higher generalization ability and prediction accuracy than the RBF, LSSVM, RNN and LSTM neural networks, with an average accuracy of 99.4%.
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