Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel

Santos, Carlos Alexandre dos; Spim, Jaime Alvares; Garcia, Amauri

doi:10.1016/s0952-1976(03)00072-1

Cited by 128 publications

(97 citation statements)

References 20 publications

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“…In order to test the accuracy and on-line performance of the model, the measured surface temperature and shell thickness were compared to the calculated ones. The parameters of caster and thermal physical properties of Q235 steel 6,15,16) used in calculation are shown in Tables 1-3 respectively. The water distribution coefficients in each secondary cooling zone in Eq.…”

Section: Resultsmentioning

confidence: 99%

“…So the quality of billet has been improved. 15,17,18) The careful control of the strand surface temperature at the straightening point is very important in the casting operation. The strand surface must be at a temperature outside the low ductility trough observed in steels and at a temperature either higher than the high-temperature limit of the ductility trough or lower than the low limit in order to avoid transverse surface cracking and other defects.…”

Section: Application In the Plant Operationmentioning

confidence: 99%

“…Limiting the strand surface above the upper limit of the low ductility temperature, transversal cracks are also reduced. 15) In this work it was considered that the strand surface temperature is kept above the upper limit of the low ductility range, and the strand surface temperature at the straightening point is about 1 050°C depending on the carbon content. point, meanwhile the strand surface temperature fluctuates slightly.…”

Section: Application In the Plant Operationmentioning

confidence: 99%

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Applying of Real-time Heat Transfer and Solidification Model on the Dynamic Control System of Billet Continuous Casting

2008

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In this paper, a real-time mathematical heat transfer model for billet continuous casting of low carbon steel has been presented and solved by finite volume method. This model can be used to simulate the casting conditions which vary frequently. As taking the variation of superheat of liquid steel into consideration in the water flow rate distribution, the fluctuation of billet surface temperature has been decreased. Meanwhile the fluctuation of measured temperature has been largely reduced when it is measured with thermal imager. The mathematical model has been validated by measuring shell thickness and surface temperature in industrial caster. The on-line model can monitor surface temperature and shell thickness along the caster machine in the casting process, and it has been applied on the dynamic control system to adjust the operation parameters. The billet defect has been decreased greatly and the quality of billet has been obviously improved.KEY WORDS: real-time model; continuous casting; heat transfer; solidification; secondary cooling; dynamic control.......... (1) where T is the billet temperature, t is the time, x, y are the rectangular coordinates, k is the thermal conductivity of steel, c is the specific heat capacity of steel, r is the density of steel, and q is the latent heat source.Owing to the symmetry of heat transfer in square sections, one quarter of billet cross-section is selected as calculation area. The grid division and boundary conditions for cross-section of the billet are presented in Fig. 1. The P is the control volume, and w, e, n, s are the correspondence interfaces.In the time interval (t, tϩDt), Eq. (2) is obtained by finite volume method and the discrete equation group of complete implied format is formulated. 8,9) ... Thermophysical Properties of SteelThe latent heat is generated during phase change between liquidus and solidus temperatures of steel and is ex- where L is the latent heat of fusion, r is the density of steel, f s is the solid fraction in the mushy zone and it can be expressed in accordance with Eq. (4) Equations (4) and (1) giveThe f s 3) of low carbon steel can be expressed by the following equation:... (6) where T L is the liquidus temperature of steel, T S is the solidus temperature of steel.The convective heat transfer in the liquid pool and mushy zone is quite difficult to be calculated by using differential equations. The effective thermal conductivity is given by the following linear relationship 5) :where k eff is the effective thermal conductivity, k is the thermal conductivity of solid steel, A is a constant, and f s is the solid fraction in the mushy zone.The specific heat capacity and density are expressed by the following equations: where sub-indices S and L, respectively, indicates solid and liquid state. The Boundary Conditions (1) Initial ConditionThe boundary condition to the steady state casting has been assumed to be the same as the pouring or casting temperature (T 0 ) of the steel. (10) where T 0 is the steel casting temperature, which is ...

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Section: Resultsmentioning

confidence: 99%

Section: Application In the Plant Operationmentioning

confidence: 99%

Section: Application In the Plant Operationmentioning

confidence: 99%

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Applying of Real-time Heat Transfer and Solidification Model on the Dynamic Control System of Billet Continuous Casting

2008

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“…Gravela et al [1] presented ant colony optimization (ACO) for the solution of an industrial scheduling problem in an aluminum casting center. Santos et al [2] presented the development of a computational algorithm (software) applied to maximizing the quality of steel billets produced by continuous casting. A mathematical model of solidification works integrated with a genetic search algorithm and a knowledge base of operational parameters.…”

Section: Introductionmentioning

confidence: 99%

Casting Improvement Based on Metaheuristic Optimization and Numerical Simulation

Radiša¹,

Dučić

Manasijević³

et al. 2017

FU Mech Eng

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Abstract. This paper presents the use of metaheuristic optimization techniques to support the improvement of casting process. Genetic algorithm (GA), Ant Colony Optimization (ACO), Simulated annealing (SA) and Particle Swarm Optimization (PSO) have been considered as optimization tools to define the geometry of the casting part's feeder. The proposed methodology has been demonstrated in the design of the feeder for castingPelton turbine bucket. The results of the optimization are dimensional characteristics of the feeder, and the best result from all the implemented optimization processes has been adopted. Numerical simulation has been used to verify the validity of the presented design methodology and the feeding system optimization in the casting system of the Pelton turbine bucket.

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“…Furthermore, in [29] the mold parameters were optimized to maximize casting velocity using genetic algorithms and other techniques. In addition, the cooling conditions were optimized in [30,31] by employing a knowledge base of operational parameters, a genetic algorithm and weighing method. More recently in [32], an evolutionary neural network approach (PPNNGA) was used to pick the optimum network model for representing the objective functions as a metamodel.…”

Section: Introductionmentioning

confidence: 99%

New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

Sindhya

Miettinen

2011

Materials and Manufacturing Processes

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In this article, we present a new perspective in solving computationally demanding problems such as the optimal control of the continuous casting of steel. We consider a multiobjective formulation of the optimal control of the surface temperature of the steel strand with five objectives, where constraint violations are minimized as objectives because no feasible solutions exist otherwise. A hybrid evolutionary multiobjective algorithm (HNSGA-II) is used to overcome discrepancies of evolutionary multiobjective optimization algorithms such as slow convergence and lack of convergence proof to the Pareto front. HNSGA-II uses NSGA-II as an underlying evolutionary multiobjective algorithm and an achievement scalarization function to scalarize objective functions for local search, which improves the population and speeds up the convergence. This is important because most evolutionary multiobjective algorithms are known to have difficulties with five objective functions. The algorithm is used to generate a set of Pareto solutions showing different trade-offs. However, it is difficult to generate Pareto solutions showing all the different trade-offs with a finite small population size. Hence, we use the preference information related to constraint violations in the weights of the achievement scalarization function to generate solutions with different trade-offs in preferable regions of the Pareto front. In addition, we use clustering and present typical solutions of different clusters so that the most preferred solution to be implemented can easily be identified. We demonstrate the approach and compare the results to previous studies.QC 2012020

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Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel

Cited by 128 publications

References 20 publications

Applying of Real-time Heat Transfer and Solidification Model on the Dynamic Control System of Billet Continuous Casting

Applying of Real-time Heat Transfer and Solidification Model on the Dynamic Control System of Billet Continuous Casting

Casting Improvement Based on Metaheuristic Optimization and Numerical Simulation

New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

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