A solidification heat transfer model and a neural network based algorithm applied to the continuous casting of steel billets and blooms

This work presents the development of a computational algorithm applied to improve the thermal behaviour in the secondary cooling zone of steel billets and blooms produced by continuous casting. A mathematical solidification heat transfer model works integrated with a neural network based algorithm (NNBA) connected to a knowledge base of boundary conditions of operational parameters and metallurgical constraints. The improved strategy selects a set of cooling conditions (in the secondary cooling zone) and metallurgical criteria established to attain high product quality, which are related to a more homogeneous thermal behaviour during solidification. Initially, the results of simulations performed by using the mathematical model are validated against experimental industrial data, and good agreement is observed, in any case examined, permitting the determination of nominal heat transfer conditions by the inverse heat conduction method. By using the numerical model linked to a NNBA results have been produced determining a set of casting conditions, which has permitted better strand surface temperature profile and metallurgical length to be attained during the continuous casting of SAE 1007 billets and SAE 1025 blooms.

[1]  S. T. Hsu,et al.  Engineering heat transfer , 1963 .

[2]  J. A. Spim,et al.  A modified network approach for modeling solidification of complex-shaped domains , 2000 .

[3]  Carlos Alexandre dos Santos,et al.  Determination of transient interfacial heat transfer coefficients in chill mold castings , 2001 .

[4]  I. V. Samarasekera,et al.  Taper design in continuous billet casting using artificial neural networks , 1998 .

[5]  J. A. Spim,et al.  Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel , 2003 .

[6]  Nirupam Chakraborti,et al.  A study of the continuous casting mold using a pareto-converging genetic algorithm , 2001 .

[7]  Amauri Garcia,et al.  The use of a heuristic search technique for the optimization of quality of steel billets produced by continuous casting , 2001 .

[8]  B. A. Pollock.  Sleeping with the enemy , 1994 .

[9]  J. A. Spim,et al.  The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel , 2002 .

[10]  R. Noyé,et al.  Numerical Solutions of Partial Differential Equations , 1983 .

[11]  H. K. D. H. Bhadeshia,et al.  Neural Networks in Materials Science , 1999 .

[12]  K. Suresh Kumar,et al.  A heat transfer study of the continuous caster mold using a finite volume approach coupled with genetic algorithms , 2003 .

[13]  G Alvarez de Toledo,et al.  Model optimization of continuous casting steel secondary cooling , 1993 .

[14]  I. V. Samarasekera,et al.  The pursuit of steel billet quality , 1994 .

[15]  Lorenz T. Biegler,et al.  Optimization and continuous casting: Part I. Problem formulation and solution strategy , 1991 .

[16]  Eric Hervieu,et al.  Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model , 2002 .

[17]  I. V. Samarasekera,et al.  Knowledge Engineering an Expert System to Trouble-Shoot Quality Problems in the Continuous Casting of Steel Billets , 1992 .

[18]  Lorenz T. Biegler,et al.  Optimization and continuous casting: Part II. Application to industrial casters , 1991 .

[19]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[20]  J. K. Brimacombe,et al.  The challenge of quality in continuous casting processes , 1999 .