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2023, Volume 24, Issue 3

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Frontiers of Information Technology & Electronic Engineering >> 2023, Volume 24, Issue 3 doi: 10.1631/FITEE.2200366

Data-driven soft sensors in blast furnace ironmaking: a survey

Affiliation(s): State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan; Research Institute, Baoshan Iron & Steel Co., Ltd., Shanghai 201900, China; less

Received: 2022-08-30 Accepted: 2023-03-25 Available online: 2023-03-25
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Abstract

The is a highly energy-intensive, highly polluting, and extremely complex reactor in the . are a key technology for predicting molten iron quality indices reflecting energy consumption and operation stability, and play an important role in saving energy, reducing emissions, improving product quality, and producing economic benefits. With the advancement of the Internet of Things, big data, and artificial intelligence, data-driven in es have attracted increasing attention from researchers, but there has been no systematic review of the data-driven in the . This review covers the state-of-the-art studies of data-driven technologies in the . Specifically, we first conduct a comprehensive overview of various data-driven soft sensor modeling methods (multiscale methods, adaptive methods, , etc.) used in ironmaking. Second, the important applications of data-driven in ironmaking (silicon content, molten iron temperature, gas utilization rate, etc.) are classified. Finally, the potential challenges and future development trends of data-driven in ironmaking applications are discussed, including digital twin, multi-source data fusion, and carbon peaking and carbon neutrality.

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