A dynamic analytics method based on multistage modeling for a BOF steelmaking process
journal contributionposted on 16.11.2018, 13:16 by Chang Liu, Lixin Tang, Jiyin LiuJiyin Liu, Zhenhao Tang
This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model's accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a basic oxygen furnace steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy.
This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0901900, in part by the Fund for Innovative Research Groups of the National Natural Science Foundation of China under Grant 71621061, in part by the National Natural Science Foundation of China through the Major International Joint Research Project under Grant 71520107004, in part by the Major Program of National Natural Science Foundation of China under Grant 71790614, and in part by the 111 Project under Grant B16009.
- Business and Economics