A novel intelligent monitoring method for the closing time of the taphole of blast furnace based on two-stage classification. (April 2023)

• Record Type: Journal Article
• Authors: Jiang, Zhaohui; Dong, Jinzong; Pan, Dong; Wang, Tianyu; Gui, Weihua
• Is Part Of: Engineering applications of artificial intelligence. Volume 120(2023)
• Publication Date: 2023-04
• Subjects: Blast furnace -- Closing taphole time -- Molten iron flow -- Two-stage classification -- Multimodal data fusion -- Intelligent monitoring; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105849
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Determining the taphole closing time is an essential task in the blast furnace ironmaking process because the closing time directly affects the efficiency of iron production and the stability of the blast furnace. However, at present, the taphole closing time in most ironmaking plants is judged by on-site workers based on experience, which lacks scientific guidance. To determine the taphole closing time intelligently and accurately, a novel monitoring method is proposed, which innovatively simplifies the monitoring problem of the absolute taphole closing time into a two-stage classification problem of relative tapping state. In the first stage, a classification algorithm SE-ResNeXt, which only takes the molten iron flow image data as the input data, is used to preliminarily determine the current molten iron flow state in the time dimension during tapping. When it is recognized that the molten iron flow is in the last tapping state in the first stage, the second stage is carried out. In the second stage, a novel multimodal data fusion network SENeXt-Decoder consisting of a novel image feature extraction module, a novel fusion module and a multi-head attention decoder is proposed to obtain the exact taphole closing time, which fuses the molten iron flow image data and blast furnace operating state data. The comparison experiment with the actual taphole closing time on site shows that the absolute monitoring error of this method is within 120 s, and the relative monitoring error is within 1.2%, which better meets the factory's demand for error accuracy.