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Based on a coupled heat and stress model, an artificial intelligence optimisation program was developed to optimise the process parameters in the continuous casting of steel. The program can be used to identify the key factors for the cracks of the strand and obtain the optimum process parameters of continuous casting. The optimisation program contains two models, one is the thermomechanical model developed using the finite difference method, and the other is the optimisation model developed using the subproblem approximation method. The whole program works by automatic iteration between the two models. This study has taken the stress constraint in the solidified shell into consideration when studying the metallurgical constraints. The application of the achieved optimum parameters would make it possible to run the caster at maximum productivity, with minimum cost and fewer defects. After manufacturing verification of this optimisation project, the incidence of cracks has reduced from 8% to 2%, and water consumption in the secondary cooling zone has been decreased by 25%. [ABSTRACT FROM AUTHOR]

The present work aims to simulate the solidification process during beam blank continuous casting, to determine the causes of defects, and to optimise the process parameters. A transient finite element model has been developed to compute the temperature and stress profile in beam blank continuous casting. It has been found that the surface temperature fluctuated greatly and that the equivalent stress and strain have their highest values at the flange tip and the web. By comparing the calculated values with metallurgical constraints, the key factor causing surface cracks on the web was determined and an optimum cooling regime was proposed. Online verification of this optimisation project has led to a reduction in the incidence of web surface cracks. [ABSTRACT FROM AUTHOR]