Application of support vector machine method in prediction of Kappa number of kraft pulping process

The predicting Kappa number of kraft pulping process is very difficult due to the complicated process kinetics and poor basic information. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory was introduced. The basic theory and algorithm of the method were presented and application of the method to predict Kappa number was conducted. In the meantime, the comparison was made between SVM methods and the traditional methods (linear regression and artificial neural network). The comparative result indicated that SVM method was high in precision, faster in computation and had a better generalization ability.