The prediction of health status and remaining life of in-service SCR catalysts is urgent for scientific maintenance management and safe use of the de-NOx system. According to the grey characteristics of sample data of the catalyst activity and service time

a gray forecasting model and various curve fitting model were established based on activity data of in-service catalysts in thermal power plants. Moreover

relative catalyst activity was then predicted depending on whether the data were in equidistant time distribution. The results show that

when the catalyst activity measurement time met the equidistant time requirement

using the GM(1

1) model could achieve good prediction result and high model precision. When the measurement time didn't meet the equidistant time requirement

the second order polynomial model was effective. Overall

the established models possessed accurate capabilities in predicting the in-service catalyst life

which provided references for the life management of SCR catalysts in thermal power plants.