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

The control of quality in continuous casting products cannot be achieved without a knowledge base which incorporates parameters and variables of influence such as: equipment characteristics, steel, each component of the system and operational conditions. This work presents the development of a computational algorithm (software) applied to maximize 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. The optimization strategy selects a set of cooling conditions (mold and secondary cooling) and metallurgical criteria in order to attain highest product quality, which is related to a homogeneous thermal behavior during solidification. The results of simulations performed using the mathematical model are validated against both experimental and literature results and a good agreement is observed. Using the numerical model linked to a search method and the knowledge base, results can be produced for determining optimum settings of casting conditions, which are conducive to the best strand surface temperature profile and metallurgical length. (C) 2003 Elsevier Ltd. All rights reserved.