Therefore, this paper presents a literature review on the characteristics of different end-point carbon prediction models and auto-detection methods in potential applications for BOF steelmaking. Adapted from ref.Įnd-point carbon prediction with good generalization and high accuracy would certainly improve the productivity and steel quality that can be achieved in BOF steelmaking. Therefore, the accurate prediction and control of end-point carbon content in liquid steel is still a challenge for BOF steelmaking, especially for medium-high carbon steel smelting. The complicated interaction between various influential factors determines the end-point carbon content in molten steel. It is very difficult to monitor and collect data during the BOF steelmaking process due to the high temperature, aggressive chemical environment, and the violent motion of the mass. From a process control perspective, the decarburization of hot metal in BOF steelmaking is extremely complex since it takes place at high temperature and contains turbulent multiphase flow and chemical reactions.
Meanwhile, the fluxes such as lime and dolomite are added to the vessel to slag, and impurities were removed. įigure 1 illustrates the schematic representation of BOF steelmaking a charge of hot metal and scrap is rapidly refined into the liquid steel with desired carbon content through the oxidation of supersonic oxygen injection from top lance and stirring of bottom gas from bottom tuyeres. As one of the key operations, precise prediction and control of end-point carbon content in molten steel would greatly promote high-quality, high-efficiency, and stable production for the BOF steelmaking process. According to statistical data, the crude steel produced by BOF accounts for 70.8% of the global steel production in 2018. However, it is envisaged that the intelligent end-point carbon prediction will witness more frequent applications and greatly improve the high-quality, high-efficiency, and stable production for BOF steelmaking in the future.īasic oxygen furnace (BOF) steelmaking is the main method for the steelmaking process.
Future contributions to the development and application of intelligent end-point carbon prediction in BOF steelmaking are still arduous tasks. With the development of information technology and auto-detection methods, BOF end-point carbon prediction mainly has gone through three stages, such as static prediction, dynamic prediction, and intelligent prediction. The end-point carbon prediction for BOF steelmaking has initially relied on the experience and skill of the operators. This paper presents the characteristics of different end-point carbon prediction models. Due to numerous and diversity of the studies on BOF end-point carbon prediction, it seems necessary to provide a comprehensive literature review on state-of-the-art developments in end-point carbon prediction for BOF steelmaking. The precise prediction of end-point carbon content in liquid steel plays a critical role in increasing productivity as well as energy efficiency that can be achieved in the basic oxygen furnace (BOF) steelmaking process.
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