Prediction of denitrification system inlet nitrogen oxide based on neural network online learning

doi:10.3969/j.issn.1000-1565.2018.04.013

Abstract

Abstract: To solve the problem that Static soft sensing model of denitrification system inlet nitrogen oxides can notsatisfy the requirement of variable load,a soft sensor model based on neural network online learning was proposed.Finding the optimum solution of Static neural network by particle swarm optimization,combining prediction error with current prediction error to update weights,thresholds and learning rates online,which can satisfy the requirement of variable load,the model was verified with power plant actual data of power plant.Results show that neural network online learning model is high accuracy and good realtime,with strong generalization ability,which is able to predict the inlet nitrogen oxides,and provide a effective method for online measuremet and monitoring of inlet nitrogen oxides in NO_x reduction control system.

Key words: inlet nitrogen oxides, kernel principal component analysis, particle swarm optimization, prediction error, neural network online learning

CLC Number:

TK314

JIN Xiuzhang, ZHANG Shaokang. Prediction of denitrification system inlet nitrogen oxide based on neural network online learning[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(4): 423-431.

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