A comparative study of deep and shallow predictive techniques for hot metal temperature prediction in blast furnace ironmaking. (2nd November 2019)

• Record Type: Journal Article
• Authors: Zhang, Xinmin; Kano, Manabu; Matsuzaki, Shinroku
• Is Part Of: Computers & chemical engineering. Volume 130(2019)
• Publication Date: 2019-11-02
• Subjects: Soft-sensor -- Virtual-sensor -- Deep learning -- Shallow learning -- Hot metal temperature -- Blast furnace ironmaking; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.106575
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: In blast furnace ironmaking, it is crucial to predict hot metal temperature (HMT). Deep and shallow predictive methods were applied to an industrial blast furnace. Both the current time and multi-step-ahead HMT predictions were investigated. The prediction performance and computational time of ten methods were evaluated. Abstract: To realize stable operation of the ironmaking process, it is important to predict hot metal temperature (HMT) in a blast furnace. Recently, deep learning is emerging as a highly active area of research. Nonetheless, no thorough study has yet appeared comparing the performance of deep learning methods to the shallow learning methods in predicting HMT. This paper provides a comparative study on the deep and shallow predictive methods for the current time and multi-step-ahead HMT predictions. Three advanced deep predictive methods and seven effective shallow predictive methods are investigated from the application point of view. Both the deep and shallow predictive methods were applied to an industrial blast furnace, where the prediction performance and computational time of ten methods were evaluated. The results demonstrated that (1) shallow neural network is preferred for current time HMT prediction; (2) Gaussian process regression and support vector regression are preferred for multi-step-ahead HMT predictions.