Analyzing the Fluid Flow in Continuous Casting through Evolutionary Neural Nets and Multi‐Objective Genetic Algorithms

Abstract The flow fields computed for a typical continuous caster are analysed using the basic concepts of Pareto‐optimality in the context of multi‐objective optimization. The data generated by the flow solver FLUENT™ are trained through Evolutionary Neural Networks that emerged through a Pareto‐tradeoff between the complexity of the network and its accuracy of training. A number of objectives constructed this way are subjected to optimization using a Multi‐objective Predator‐Prey Genetic Algorithm. The procedure is repeated using the software modeFRONTIER™ and the results are compared and analysed.