Modeling effects of skid buttons and dislocated skids on the heating quality of slabs in an industrial walking-beam reheating furnace

Xu, Jianxiang; Li, Baokuan; Qi, Fengsheng; Rong, Wenjie; Kuang, Shibo

doi:10.1016/j.ijheatmasstransfer.2023.124245

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…Finally, the soaking zone considered the desired target temperature [ 42 ] and the dissolution of carbides or nitrides in the billet [ 43 ]. Additionally, to ensure the temperature homogeneity of a billet for subsequent hot rolling, the thermal loss of the billet by the skid system in the reheating furnace should be considered in this soaking zone [ 44 ]. Although similar heating patterns proposed in this study have been widely used in the industrial field [ 13 , 42 ], it is still being utilized with a lack of theoretical background.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Thermal Behaviors of Carbon and Stainless Steel Billets during the Heating Process

Hwang

2023

Materials

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The effect of thermal properties on the thermal behavior of a steel billet was investigated during the heating process to understand each effect and to provide process engineers with sufficient data to choose the optimal design conditions in reheating the furnace for hot rolling. Carbon steel and austenitic stainless steel (STS) were compared based on numerical simulations owing to the completely different thermal properties of these two steels: carbon steel having a phase transformation with a relatively high thermal conductivity and STS having no phase transformation with a relatively low thermal conductivity. The thermal conductivity affected the thermal behavior of the billet in the initial stage of heating owing to the high temperature difference between the surface of the billet and the gas in the furnace, i.e., the high Biot number. Accordingly, a non-firing zone and/or a preheating zone with a low gas temperature are necessary for high-alloyed steels including STS because the thermal conductivity of these steels is relatively low. The phase transformation of the carbon steels needs to occur in the primary heating zone, and this zone needs to have a relatively low gas temperature to reduce the temperature deviation or thermal stress in the billet. The heating pattern of the carbon steels and STSs in the reheating furnace should be designed differently considering the thermal conductivity and latent heat by the phase transformation of steels to obtain a high heating quality for the billet.

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Section: Resultsmentioning

confidence: 99%

Comparison of Thermal Behaviors of Carbon and Stainless Steel Billets during the Heating Process

Hwang

2023

Materials

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“…Peck [ 6 ] presented a novel reduced-order transfer-function-based mathematical model for temperature uniformity of a gas powered industrial box furnace, demonstrating that fine adjustments to the controller can reduce temperature rise time while maintaining compliance with Temperature Uniformity Survey (TUS) standards. Xu [ 7 ] established a three-dimensional turbulent flow fluid–structure interaction heat transfer model for pit furnaces. By analyzing the flow field and temperature field, the study provides a wall function method to improve the annealing temperature uniformity.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling for 3D Transient Temperature Field of Large-Scale Aluminum Alloy Workpieces Based on Multi-Loss Consistency Optimization PINN

Shen

Chen

Wang

et al. 2023

Sensors

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Uniform temperature distribution during quenching thermal treatment is crucial for achieving exceptional mechanical and physical properties of alloy materials. Accurate and rapid prediction of the 3D transient temperature field model of large-scale aluminum alloy workpieces is key to realizing effective thermal treatment. This paper establishes a 3D transient temperature field model of large aluminum alloy workpieces and proposes a multi-loss consistency optimization-based physics-informed neural network (MCO-PINN) to realize soft sensing of the 3D temperature field model. The method is based on a MLP structure and adopts Gaussian activation functions. A surrogate model of the partial differential equation (PDE) is first constructed, and the residuals of the PDE, initial and boundary conditions, and observed data are encoded into the loss functions of the network. By establishing a Gaussian probability distribution model of each loss function and combining it with maximum likelihood estimation, the weight consistency optimization method of each loss function is then proposed to further improve the approximation ability of the model. To optimize the training speed of the network, an adaptive initial-value-eigenvector coding clustering (AIV-ECC) algorithm is finally proposed, which quickly determines the parameters of the Gaussian activation function, reduces the dependence on the initial value and improves the generalization performance of the network. Simulation and industrial experiments demonstrate that the proposed MCO-PINN can solve the 3D transient temperature field model with high precision and high time efficiency based on sparse measurements.

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Quantitative Uniformity Evaluation of Steel Slab Heating Temperature by Non-contact Measuring Approach

Yang,

Yu,

Wang

et al. 2024

Metall Mater Trans B

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Modeling effects of skid buttons and dislocated skids on the heating quality of slabs in an industrial walking-beam reheating furnace

Cited by 9 publications

References 32 publications

Comparison of Thermal Behaviors of Carbon and Stainless Steel Billets during the Heating Process

Comparison of Thermal Behaviors of Carbon and Stainless Steel Billets during the Heating Process

Soft Sensor Modeling for 3D Transient Temperature Field of Large-Scale Aluminum Alloy Workpieces Based on Multi-Loss Consistency Optimization PINN

Quantitative Uniformity Evaluation of Steel Slab Heating Temperature by Non-contact Measuring Approach

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