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

Govindan Deepak | Zendy; Chakraborty Suman | Zendy; Chakraborti Nirupam | Zendy

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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.

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