Boiler combustion process modeling is critical for the accuracy and reliability of combustion optimization.Firstly

a steady-state detection (SSD) algorithm is used to extract steady-state samples for building steady-state combustion process model.Considering the unbalance of samples over power load

a new method of data division

which divides the available data into training subset and test subset according to load

is proposed to improve model generalization.Then

a single factor graph analysis is conducted to determine searching range of three SVM model structural parameters.After choosing the model parameters by combining grid search with cross validation

four multiple inputs single output SVM models

including boiler efficiency

NOx emission

flue gas temperature and the unburned carbon in fly ash

are established based on the divided data.The results are demonstrated that four models all have a good generalization capability.