Model-Based Tuning of Process Parameters for Steady-State Steel Casting

We present an empirical study of process parameter tuning in industrial continuous casting of steel where the goal is to assure the highest possible quality of the cast steel through proper parameter setting. The process is assumed to be under steady-state conditions and the considered optimization task is to set 18 coolant flows in the caster secondary cooling zone to achieve the target surface temperatures along the slab. A numerical model of the casting process was employed to first investigate the properties of the parameter search space, and then iteratively improve parameter settings. For this purpose, two stochastic optimization algorithms were used: a steady-state evolutionary algorithm and next-descent local optimization. The results indicate the difficulty of the optimization task arises not from a complicated fitness landscape but rather from high dimensionality of the problem.