According to serious decay of NO_xemission analyzer used in thermal power plants

the principal component analysis and multivariable process monitoring method were employed to select the dominant factors influencing the NO_xconcentration at entrance of the denitration reactor.Then

on the basis of the analyzer's decay time

the NO_xcontent data of the flue gas at inlet of the reactor was selected as the pedagogic signal to establish the prediction model for NO_x concentration in flue gas at inlet of the reactor

by using the least squares support vector machine(LS-SVM).Moreover

combing with the historical operation data of a power plant

the above model was trained and tested.The results indicate that

using the principal component analysis and multivariable process monitoring method to select the dominant factors from multiple auxiliary variables can simplify the model and make the model have a good training ability and generalization ability.This provides a feasible method for accurate and timely measurement of the NO_x content in flue gas at the reactor inlet.