Lean Operations Strategy to Combat Uncertainties in Temperture at BOF End Point, Tapping, Deoxidation, Alloy Addition and Thermal History

Shukla, Ishani; Rajesh, G.; Agarwal, Pratik; Shukla, Ajay Kumar; Philip, Deepu

doi:10.1007/978-3-319-48234-7_30

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Step4: Update ladle usage times and available time according to the selection of dephosphorization ladle (increase ladle usage times by 1, and update the available time of dephosphorization ladle to the start time of decarbonization converter of the furnace); If go to Step5, otherwise, go to Step3; Paper [8] studies a static BOF end-point control model to minimize the use of ladle furnaces during steelmaking.…”

Section: The Rule-based Ladle Selection Algorithm For Dephosphorizati...mentioning

confidence: 99%

Research on a wireless sensor network method in a real-time system

Zhang¹,

Chen²,

Wang³

et al. 2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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Section: The Rule-based Ladle Selection Algorithm For Dephosphorizati...mentioning

confidence: 99%

Research on a wireless sensor network method in a real-time system

Zhang¹,

Chen²,

Wang³

et al. 2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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“…Paper [7] studied the static basic oxygen furnace endpoint control model to minimize the use of ladle furnaces during steelmaking. Paper [8] proposed a ladle schedule based on a production plan and ladle waiting time analysis.…”

Section: Introductionmentioning

confidence: 99%

Steelmaking-Casting of Molten Steel by Decarburization Ladle Matching

Liu

Pang

et al. 2018

Mathematical Problems in Engineering

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Steelmaking-continuous casting is a complex process. The method of selecting a ladle, which also functions as a storage device, follows a specific process of the production plan. In ladle matching, several ladle attributes are considered. However, matching objectives are difficult to achieve simultaneously. Different molten steel properties have contributed to the complexity of matching constraints, and, thus, matching optimization is regarded a multiconflict goal problem. In the process of optimization, the first-order rule learning method is first used to extract key ladle attributes (performance indicators), including highest temperature, usage frequency, lowest-level material, and outlet. On the basis of a number of indicators, such as ladle temperature, quantity, material, and usage frequency, as well as skateboard quantity, the ladle matching model is established. Second, the rule of ladle selection is determined by the method of least-generalization rule learning. Third, a simulation experiment is carried out according to various scheduling order strategies and matching priority combinations. Finally, the heuristic ladle matching method based on the rule priority (RP) is determined for possible industrial applications. Results show that the accuracy of ladle selection can be improved. In particular, the numbers of ladles and maintenance times are reduced. Consequently, furnace production efficiency is also enhanced.

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Lean Operations Strategy to Combat Uncertainties in Temperture at BOF End Point, Tapping, Deoxidation, Alloy Addition and Thermal History

Cited by 2 publications

References 2 publications

Research on a wireless sensor network method in a real-time system

Research on a wireless sensor network method in a real-time system

Steelmaking-Casting of Molten Steel by Decarburization Ladle Matching

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