Control of Heat Transfer in Continuous Casting Process Using Neural Networks

Bouhouche, Salah; Lahreche, M.; Bast, Jürgen

doi:10.1016/s1874-1029(08)60034-8

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…An interesting approach of controlling the continuous casting process by using artificial neural networks (ANN) is presented by Bouchouche et al [3]. They designed and tested an ANN closed-loop control scheme of heat transfer which may be applied only in the continuous casting process.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of thermal field dynamics of mould in casting using artificial neural networks

et al. 2018

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Manufacturing a large number of cast parts made of aluminium alloy led to an increased interest in developing and applying new control techniques of the casting process. Anyway, the difficulty in estimating some important process parameters only allowed the use of some approaches which are limited to a few geometric models. Many researchers made great efforts to find the best method for monitoring and measuring thermal field dynamics of the cast and mould during solidification and cooling of the melt alloy. Acquiring very accurate data leads to best approach for solving the heat transfer problem in casting. The paper presents the prediction of thermal field dynamics of mould in permanent mould casting using artificial neural networks and based on thermal history of the cast part and the way this thermal history influences the thermal changes of the mould. It is very important to identify the relation between the thermal fields' dynamics of both cast and mould in order to create and use a control technique of the cast solidification and cooling. The necessity of controlling the cast solidification is due to the large demand of cast parts with improved mechanical properties.

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Section: Introductionmentioning

confidence: 99%

“…The solidification simulations of the cast parts have a tremendous applicability both for designing the casting process and improving the cast part quality [3].…”

Section: Introductionmentioning

confidence: 99%

Prediction of thermal field dynamics of mould in casting using artificial neural networks

et al. 2018

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“…Moreover, the secondary cooling is an important factor affecting the quality of the slab, and this has been investigated by numerous researchers worldwide over a long period. [1][2][3][4][5][6][7] A mathematical model of the heat transfer for slab casting was established on the basis of the research on the heat transfer process, and the corresponding numerical simulation and control software were then developed. Satisfactory results were achieved using optimisation technology [8][9][10] for the secondary cooling system on CCM2 at the no.…”

Section: Introductionmentioning

confidence: 99%

Optimal control of secondary cooling for medium thickness slab continuous casting

Liu

et al. 2011

Ironmaking & Steelmaking

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A software to simulate the solidification, heat transfer and water flowrate distribution in slab continuous casting was developed by establishing a mathematical model for the heat transfer and solidification in medium thickness slab casting. This model was validated by pin shooting and surface temperature measurement experiments. A reasonable target surface control temperature was found by testing the high temperature mechanical properties of Nb bearing ship plate steel, and then the water flowrate of each loop of the secondary cooling zone was determined by the software. The influence of uneven secondary cooling in the slab width direction on the quality of the slab was also investigated, which provided data for the optimisation of the secondary cooling of the slab caster. On the basis of the above research, an optimisation scheme for a secondary cooling system was proposed. Experimental results showed that the quality of the slab was significantly improved after optimisation. The centreline macrosegregation was reduced, and the ratio of equiaxed grains was increased by 3?18%. In addition, the transverse cracking of the slab was almost eliminated. List of symbolsa, b constant (a is 15?8847, and b is 0?011495) c specific heat capacity of steel, J kg 21 uC 21 c s , c l , c sl specific heats of solid steel, liquid steel and steel in the mushy zone, J kg 21 uC 21 c w specific heat capacity of water, J kg 21 uC 21 E elastic modulus, GPa f s solid phase fraction g gravity constant (9?8 N kg 21 ) h heat transfer coefficient, W m 22 uC 21 h w , h n complex heat transfer coefficients on the broad and narrow surfaces of slab, W m 22 uC 21 H the distance between meniscus and force bearing point, m L f latent heat, J kg 21 L m effective length of mould, m L the distance between meniscus and microunit, m q heat flux, W m 22 -q average surface heat flux, W m 22 Q m water flowrate of mould, kg s 21 S eff effective cooling region of mould, m 2 t temperature, uC t b , t w , t e temperatures of strand surface, cooling water and environment, uC T l , T s liquidus and solidus of steel, uC v casting speed, m min 21 W the flux of cooling water, L m 22 s 21 DP ferrostatic pressure, N m 22 DT w temperature difference between inlet and outlet water of mould, uC Dx, Dy mesh size of slab in its width and length directions (8 and 4 mm in this paper) e radiation coefficient (0?8 in this paper) l thermal conductivity coefficient, W m 21 uC 21 l s , l l , l sl thermal conductivity coefficients of solid steel, liquid steel and steel in the mushy zone, W m 21 uC 21 m Poisson's ratio r density of steel, kg m 23 s Stefan-Boltzmann constant (5?67610 28 W m 22 K 24 ) t time, s

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“…1,2) The solidification of molten steel, with non-steady-state characteristics, can be on-line controlled and monitored through real-time forecasting and feedback controlling by computer simulation model. [3][4][5][6][7][8] The factory operators are able to acquaint clearly themselves with the real slab state of heat and stress through the forecast and the control of solidification heat transfer. Thus the operators can adjust the operating parameters to make the process of continuous casting more flexible under the limitation of metallurgical criteria.…”

Section: Introductionmentioning

confidence: 99%

Development and Application of Dynamic Soft-reduction Control Model to Slab Continuous Casting Process

et al. 2010

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the effect of casting history during slab cooling process. Considering the current actual casting speed, the effective casting speed is defined based on the quantity of slicing units and the average casting speed at the secondary cooling zone. The definition can fully reflect the casting history (calculated from the beginning of the slicing unit) of each slicing unit and its effect on the total heat transfer of the caster at the secondary cooling zone. The schematic of method for calculating the effective casting speed is shown in Fig. 2. In the current calculation cycle, the secondary cooling zone totally contains N slicing units (corresponding serial numbers are j, jϩ1 · · · jϩNϪ2 and jϩNϪ1). The slicing unit generates at the meniscus and moves toward the out gate of the caster with certain casting speed.The average casting speed of slice unit j in m/s can be represented by: where L is the casting distance from meniscus in m; t and (tϩDt) are the last and the current calculation period respectively in second; dZm is the moving distance of slice unit j during the current calculation period Dt in m, which is calculated by:. .(7)Where V c is the actual casting speed in m/s. The average casting speed of all the slice units at the secondary cooling zone i can be obtained by Eq. (6). And then the average casting speed of the secondary cooling zone i can be calculated by:..... (8) where V z,i is the total average casting speed of the secondary cooling zone in m/s; N is the number of the slice units.A reasonable fractional factor is necessarily introduced to describe the relationship between the collectivity average casting speed, which can be calculated from the average casting speed contained in each unit in all sections, and the current actual speed:........... (9) where V eff,i is the effective casting speed of secondary cooling zone i in m/s; C frac (i) is the fractional factor of secondary cooling zone i; and V c is the actual casting speed in m/s. Taking into account of the remarkable differences of the distance from the meniscus to the individual secondary cooling partition, and the time that the slicing units with new operating conditions need to move to the individual secondary cooling partition, the value of cooling fractional factor must acclimatize to this technical character. For example, the front parts of secondary cooling zone are closer to the meniscus than other parts, the actual casting speed has more effects on the effective casting speed, and thus the value of fractional factor should be smaller. Contrarily, for the secondary cooling partition which have relatively longer distances to the meniscus, the actual casting speed contribute less to the effective casting speed, the value of corresponding fractional factor should be an appropriate larger number.Besides considering the history of casting speed, the concept of comparing the target surface temperature to modify the flow rate of cooling water is introduced in the control algorithms. In other words, it is to compare and analyze the theoretica...

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Control of Heat Transfer in Continuous Casting Process Using Neural Networks

Cited by 15 publications

References 5 publications

Prediction of thermal field dynamics of mould in casting using artificial neural networks

Prediction of thermal field dynamics of mould in casting using artificial neural networks

Optimal control of secondary cooling for medium thickness slab continuous casting

Development and Application of Dynamic Soft-reduction Control Model to Slab Continuous Casting Process

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