Premium
Analyzing the Fluid Flow in Continuous Casting through Evolutionary Neural Nets and Multi‐Objective Genetic Algorithms
Author(s) -
Govindan Deepak,
Chakraborty Suman,
Chakraborti Nirupam
Publication year - 2010
Publication title -
steel research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.603
H-Index - 49
eISSN - 1869-344X
pISSN - 1611-3683
DOI - 10.1002/srin.200900128
Subject(s) - artificial neural network , context (archaeology) , genetic algorithm , computer science , solver , pareto principle , multi objective optimization , flow (mathematics) , evolutionary algorithm , mathematical optimization , algorithm , mathematics , artificial intelligence , machine learning , paleontology , geometry , biology
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.