Dynamic scheduling system for steelmaking-refining-continuous casting production

Pang, Xinfu; Jiang, Yingchun; Gao, Liang; Tang, Beibei; Li, Haibo; Yu, Shengping; Liu, Wei

doi:10.1109/ccdc.2017.7979329

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The trajectories and distributions of the inclusions (1 µm~50 µm) were simulated using the Discrete Phase Model (DPM). In the model, the density of inclusions is 3000 kg/m 3 , and the diameter distribution of particles is discontinuous and is divided into 11 sizes, including 1,5,10,15,20,25,30,35,40,45,50 µm. In the integral Lagrangian coordinate system, the trajectory of an inclusion is described by the particle force differential equation.…”

Section: Governing Equations and Boundary Conditionsmentioning

confidence: 99%

“…Owing to the increasingly stringent quality requirements, many studies have investigated the equipment, process, and theoretical principles of continuous casting, particularly for the tundish, which is the final metallurgical element reactor [1][2][3][4][5][6]. To improve the removal of inclusions by the tundish, many flow control devices, such as the turbulence inhibitor, weir-dam, baffle with holes, and filter and gas-curtain device, have been developed to optimize the flow field, increase the residence time, and promote inclusions' removal [7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Removal Mechanism of Microscale Non-Metallic Inclusions in a Tundish with Multi-Hole-Double-Baffles

Jin

Dong

et al. 2018

Metals

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To effectively remove microscale inclusions in the tundish, the Multi-Hole-Double-Baffles (MHDB), a novel flow control device in the tundish for continuous casting, was developed. The hole array mode of the MHDB will directly affect the trajectories of the inclusions. The effect and removal mechanism of the inclusions with sizes of 1 µm to 50 µm in the tundish with MHDB were studied by numerical simulation. The hole array mode of MHDB could affect the inclusions' trajectories and distribution, and the mechanism underlying the effect of the MHDB was investigated using the discrete phase model (DPM). A 1:2.5 physical model was built to verify the accuracy of numerical simulation. The results showed that micro-inclusions were primarily driven by the drag force exerted by the molten steel flow, micro-inclusion trajectories followed the molten steel streamlines almost exactly, but buoyancy still played a role in the removal of the micro-inclusions near the molten steel surface; the hole array mode affected the trajectories of the micro-inclusions and controlled and decelerated the flow of molten steel, increasing the residence time of the molten steel flow a the value that is 15 times larger than the theoretical value; and "upper-in-lower-out" type MHDB showed the most efficient removal of micro-inclusions, with the removal rate being increased by 13-49% compared to the removal rates for the other type MHDB. The results of numerical simulation were well verified by physical simulation.

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Section: Governing Equations and Boundary Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Removal Mechanism of Microscale Non-Metallic Inclusions in a Tundish with Multi-Hole-Double-Baffles

Jin

Dong

et al. 2018

Metals

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Planning and Scheduling of Electric Arc Furnace Based Steelmaking

Niyayesh,

Valilai,

Uygun

2024

Engineering Materials

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Predicting endpoint parameters of electric arc furnace–based steelmaking using artificial neural network

Niyayesh,

Uygun

2024

Int J Adv Manuf Technol

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In steel manufacturing, the chemical composition of the raw material serves as the foundation for the properties of the final product. The objective of this study is to establish a prediction algorithm for estimating the highly nonlinear characteristics of chemical condensation of elements in an electric arc furnace. A multilayer feedforward neural network is used to estimate the fluctuations in parameters of molten steel. In this study, the prediction models utilize a synthetic dataset generated based on industrial data. An experiment was designed with seven multi-layer feed-forward neural networks with distinct architectures and optimization functions, including stochastic gradient descent and adaptive moment estimation, to evaluate the optimal architecture. The results demonstrated that the proposed method, which employs a mean squared error (MSE) loss function with a value less than 0.036, can effectively predict the amount of carbon, iron oxide composition, and temperature of molten steel, which are crucial quality parameters. This study proposes a novel method for optimizing steelmaking operations via the electric arc furnace route.

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Dynamic scheduling system for steelmaking-refining-continuous casting production

Cited by 5 publications

References 12 publications

Removal Mechanism of Microscale Non-Metallic Inclusions in a Tundish with Multi-Hole-Double-Baffles

Removal Mechanism of Microscale Non-Metallic Inclusions in a Tundish with Multi-Hole-Double-Baffles

Planning and Scheduling of Electric Arc Furnace Based Steelmaking

Predicting endpoint parameters of electric arc furnace–based steelmaking using artificial neural network

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