In order to ensure the safe and stable operation of denitrification system in coal-fired power plants

a scientific and reasonable life prediction plan must be formulated for the SCR catalysts. The deactivation of the SCR catalysts is determined by the combined effects of multiple physical and chemical factors. Therefore

it is difficult to predict the catalysts' service life by using conventional physical models and mathematical formulas.According to the characteristics of big data in power plants

this article preprocessed the raw data and established four prediction models

including curve fitting model

grey prediction model

BP neural network model and grey neural network model. Through case analysis

it is found that data preprocessing can improve the prediction accuracy. Generally

the optimized direct output model of the grey neural network shows high accuracy for the data that met the equidistant time requirement. Whereas

the BP neural network model can achieve better prediction results for the non-equidistant time data.