The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel

Santos, Carlos Alexandre dos; Spim, Jaime Alvares; Ierardi, Maria Clara Filippini; Garcia, Amauri

doi:10.1016/s0307-904x(02)00062-8

Cited by 56 publications

(38 citation statements)

References 4 publications

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“…Santos C A et al [12] represents the development of an algorithm, which incorporates heuristic search techniques for direct application in metallurgical industries, particularly those using continuous casting process…”

Section: Literature Reviewmentioning

confidence: 99%

Defect Minimization in Casting through Process Improvement-A Literature Review

S¹,

R²

2017

IOSR JMCE

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Section: Literature Reviewmentioning

confidence: 99%

Defect Minimization in Casting through Process Improvement-A Literature Review

S¹,

R²

2017

IOSR JMCE

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“…GA is regarded as a genetic optimization technique for global optimization, constrained optimization, combinatorial optimization and multi-objective optimization. GA has been used to enhance industrial engineering for achieving high throughput with quality guaranteed (Santos et al, 2002;Li et al, 2003;Al-Kuzee et al, 2004). There is a variety of evolutionary techniques and approaches of GA optimization, discussed in the research work by (Lopes et al, 1999;Ishibuchi & Yamamoto, 2002;Golez et al, 2002;de la Iglesia et al, 2003;Zhu & Guan, 2004;Goplan et al, 2006).…”

Section: Ai-based Decision Support Systemmentioning

confidence: 99%

A Hybrid Decision Model for Improving Warehouse Efficiency in a Process-oriented View

Tang¹,

Lau²

2010

Decision Support Systems

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“…Lally et al used an optimization method with heat flow and solidification model to determine the parameters that maximize the quality of final product of billet and slab casters [1][2]. Santos et al developed an intelligent optimization method of genetic algorithm (GA) with finite difference heat transfer model based on the metallurgical criterion and maximum withdraw speed to control the secondary cooling zone of slab continuous casting, and heuristic search algorithms were used to minimize the consumption water and the smooth temperature gradient in the secondary cooling process [3][4][5]. A model optimization of billet continuous casting steel secondary cooling with an implicit enthalpy mathematical solidification was developed to calculate three dimensional and stationary temperature fields and to decrease center segregation [6].…”

Section: Introductionmentioning

confidence: 99%

The optimisation of the secondary cooling water distribution with improved genetic algorithm in continuous casting of steels

Zhai

et al. 2015

Materials Research Innovations

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An improved genetic algorithm is presented for the water consumption of the secondary cooling zone based on the heat transfer model of the off-line bloom caster. This study is to control the existing cooling systems and the steel casting practises in order to produce steel with best possible quality. The fitness function of improved genetic algorithm is founded according to the metallurgical criteria. This algorithm coupled with heat transfer model and metallurgical criteria, added dynamic coding method and self-adapting mutation on the original genetic algorithm can increase water distribution adaptively and improve the process efficiency. The simulation results of T91 bloom show that the optimised distribution reduced by 2% of water consumption comparing to that of before optimisation. The maximum surface cooling rate and the rate of temperature rise reduced, and the equiaxed rate increases. The function is built for explaining the relationship between the casting speed and water distribution. Statement of noveltyIn this paper, an improved genetic algorithm is designed for the water consumption of the secondary cooling zone based on the heat transfer model of the off-line bloom caster. This algorithm coupled with heat transfer model and metallurgical criteria, added dynamic coding method and self-adapting mutation on the original genetic algorithm, can increase the simulated water distribution adaptively, improve the efficiency of the simulation process and the convergence ratio, and reduce the reheating between the zones. In the experimental, it is found that the water consumption after optimization reduced by 2%, compared to that of before optimization. The maximum surface cooling rate and the rate of temperature rise reduced, and the equiaxed rate increases. Then a function is built for explaining the relationship between casting speed and water distribution, which can be the direction to control the cooling system in manufacturing process. So far, to our knowledge, although a lot of intelligent algorithms are used to optimize the secondary cooling conditions, but there are few reports on the simulation of this process with the developed algorithms. In this study, the improved genetic algorithm to optimize the secondary cooling water distribution is concerned, and the results show that the developed algorithm can save more water consumption with lower reheating and cooling rates. AbstractAn improved genetic algorithm is presented for the water consumption of the secondary cooling zone based on the heat transfer model of the off-line bloom caster. This study is to control the existing cooling system and the steel casting practice in order to produce steel with best possible quality. The fitness function of IGA (improved genetic algorithm) is founded according to the metallurgical criteria. This algorithm coupled with heat transfer model and metallurgical criteria, added dynamic coding method and self-adapting mutation on the original genetic algorithm, can increase water distribution adaptively and...

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The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel

Cited by 56 publications

References 4 publications

Defect Minimization in Casting through Process Improvement-A Literature Review

Defect Minimization in Casting through Process Improvement-A Literature Review

A Hybrid Decision Model for Improving Warehouse Efficiency in a Process-oriented View

The optimisation of the secondary cooling water distribution with improved genetic algorithm in continuous casting of steels

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