| Abstract:Aiming at the significant difference in expectation of the pulp properties estimated by manual experience based on the ratio of waster papers used, in this paper, utilizing the field data from paper mill, two modeling methods, back propagation (BP) neural networks and support vector machine (SVM), were individually applied to the prediction of pulp brightness based on the ratio of waste papers used. The prediction models were developed using all the data set and the average data set respectively. The results showed that, in terms of the prediction accuracy, prediction stability and training time, the prediction model of pulp brightness by SVM method using the average data set not only had good prediction precision (2.42%) and fairly prediction stability (0.58%), but also the fast training process (0.2 s), which could meet the requirements of the paper mill. |
