Design and application of dynamic secondary cooling control based on real time heat transfer model for continuous casting

C., J.; Lu, C. S.; Chen, L. Y.

doi:10.1179/1743133613y.0000000088

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…In the model described by Eqs. (1) through (8), there are some machine-dependent parameters apparently appearing in the thermo-physical properties and boundary conditions, including A, B, m, ɑ i 's and ε a . They cause the main uncertainty of the model, and they should be calibrated according to the measurements in the field before application.…”

Section: Calibration To Reduce the Uncertainty Of Modelmentioning

confidence: 99%

“…In the continuous casting process of steel, heat transfer model [1][2][3][4][5][6] is of central importance for optimization and control of the key technologies, including secondary cooling [7][8][9] , electromagnetic stirring [10][11][12] and dynamic reduction 13,14) . Specifically, real-time heat transfer model 3,15,16) is the core for dynamic online control during unsteady transient state.…”

Section: Introductionmentioning

confidence: 99%

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A Three Dimensional Real-time Heat Transfer Model for Continuous Casting Blooms

et al. 2023

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Heat transfer model is the basis for control and optimization of continuous casting of steel.In this paper, a three dimensional (3D) real-time heat transfer model has been presented for continuous casting blooms, especially for the initial and final casting stages. To be real-time, an algorithm based on inheritable variable non-uniform grid and variable time steps has been developed, accelerating the 3D heat transfer model by 110~200 times. Meanwhile, the discrete parameters including grid and time step have been optimized, and the influence of central processing units (CPUs) and programming languages has also been studied. Then the relative calculation time, which is the ratio of calculation time to physical time, has been reduced to 0.62 while numerical errors are within 0.74%. Further, to reduce the uncertainty of the model, the machine-dependent parameters appearing in the thermo-physical properties and the boundary conditions have been calibrated with measurements of surface temperature by thermometer and shell-thickness by nail-shooting, then the corresponding optimization problem of minimizing the errors between calculation and measurements has been solved by particle swarm optimization algorithm. After calibration, the model's uncertainty has been obviously reduced. Finally, the calibrated model has been verified by online surface temperature measurement and it shows good agreement as the errors between calculation and measurements are less than ±10℃.

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Section: Calibration To Reduce the Uncertainty Of Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Three Dimensional Real-time Heat Transfer Model for Continuous Casting Blooms

et al. 2023

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“…The governing equation describing the internal temperature distribution of the solid is the heat conduction equation. The general form of 3D unsteady heat conduction differential equation in the rectangular coordinate system is as follows

ρ c \frac{\partial t}{\partial τ} = \frac{\partial}{\partial x} (λ \frac{\partial t}{\partial x}) + \frac{\partial}{\partial y} (λ \frac{\partial t}{\partial y}) + \frac{\partial}{\partial z} (λ \frac{\partial t}{\partial z}) + ϕ

where, t , ρ , c , τ, and ϕ are infinitesimal body temperature, density, specific heat, time, and per unit time per unit volume of the heat which is generated by the internal heat source, respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%

Numerical Simulation of Heat Transfer between Roll and Slab under Dry Secondary Cooling in Ultrathick Slab Continuous Casting

Liu

Long

et al. 2019

steel research int.

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A 3D model of heat transfer between the circular‐distributed water channel roll (CDWCR) and slab during ultrathick slab continuous casting process is established, considering the structure of water channels inside CDWCR and rotating contact between the roll and slab. The results show that the contribution of conduction heat transfer in the heating process of the roll is greater than that of radiation heat transfer, and maximum surface temperature of the roll is 460 K, located at the place where it is right out of contact with the slab, and the change of internal temperature of the roll lags behind the change of surface temperature. The temperature decreases gradually from the outside to the inside in the radial direction. The temperature of the CDWCR inside the circle of cooling water channels is no more than 325 K. The yield strength of CDWCR is analyzed, showing that the strength of the roll meets the requirements of using it in the process. In addition, the orthogonal method is used to analyze the influence factors on CDWCR temperature. The results indicate that the orders of factors affecting more are the contact angle, the slab temperature, the casting speed, and the inlet speed of cooling water.

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“…In recent years, much research has been carried out well into the heat transfer model [2][3][4][5][6][7]. Among these studies, most of the developed models are two-dimensional [8] or three dimensional but offline [9], and the steady models are applied to optimize the process parameters [10,11], while real-time transient ones are used for online dynamic control [12,13]. However, for more precise control, the model is supposed to be three dimensional and real-time.…”

Section: Introductionmentioning

confidence: 99%

Digital-Twin-Based Coordinated Optimal Control for Steel Continuous Casting Process

Yang

Liu

et al. 2023

Metals

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A digital-twin-model-based optimal control system is presented for the steel continuous casting process. The system is designed for the coordinated optimization and dynamic control of secondary cooling and final electromagnetic stirring (FEMS), and involves three related parts. Firstly, a three dimensional real-time heat transfer model is established as the digital twin of the heat transfer process of continuous casting; for high accuracy, it is calibrated offline and calibrated online using measurements of the surface temperatures and shell thicknesses (only offline). Secondly, according to metallurgical rules, cooling and stirring are optimized coordinatively, based on the established digital-twin model and chaos particle swarm optimization algorithm. Thirdly, cooling and stirring are further dynamically controlled for quality stability. Finally, the system is applied in a bloom caster with model errors ≤ ±10 °C and control errors ≤ ±4 °C, which reduces the macro-segregation over grade 1.5 from 11% to 3.3%.

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Design and application of dynamic secondary cooling control based on real time heat transfer model for continuous casting

Cited by 6 publications

References 14 publications

A Three Dimensional Real-time Heat Transfer Model for Continuous Casting Blooms

A Three Dimensional Real-time Heat Transfer Model for Continuous Casting Blooms

Numerical Simulation of Heat Transfer between Roll and Slab under Dry Secondary Cooling in Ultrathick Slab Continuous Casting

Digital-Twin-Based Coordinated Optimal Control for Steel Continuous Casting Process

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