In order to control NOx emissions and enhance boiler efficiency in coal-fired boilers,the thermal operating data from an ultra-supercritical 1 000 MW unit boiler were analyzed.On the basis of the support vector regression machine (SVM),the fruit fly optimization algorithm (FOA)was applied to optimize the penalty parameter C,ker-nel parameter g and insensitive loss coefficient of the model.Then,the FOA-SVM model was established to predict the NOx emissions and boiler efficiency,and the performance of this model was compared with that of the GA-SVM model optimized by genetic algorithm (GA).The results show the FOA-SVM model has better prediction accuracy and generalization capability,of which the maximum average relative error of testing set lies in the NOx emissions model,which is only 3 .5 9%.The above models can predict the NOx emissions and boiler efficiency accurately,so they are very suitable for on-line modeling prediction,which provides a good model foundation for further optimiza-tion operation of large capacity boilers.