Soft-sensing of Oxygen Content of Flue Gas Based on Mixed Model

In order to increase the measuring accuracy of oxygen content of flue gas, a kind of new soft-sensing method of oxygen content in flue gas based on mixed model was presented. The main body of the model was set up with support vector regression (SVR), the input set was pretreated with principal component analysis (PCA) method to reduce input number of dimensions, the training output set was pretreated with empirical mode decomposition (EMD) method to eliminate the influences caused by high-frequency interference, and model calibration was carried with K-fold cross validation (K-CV) method. The simulation result shows that this mixed model method has better accuracy and the ability of generalization than those single-models with support vector machine or neural network.