Blast furnace charging optimization using multi-objective evolutionary and genetic algorithms

Tamoghna Mitra, Frank Pettersson, Henrik Saxén, Nirupam Chakraborti

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)

Abstract

Charging programs giving rise to desired burden and gas distributions in the ironmaking blast furnace were detected through an evolutionary multi-objective optimization strategy. The Pareto optimality condition traditionally used in such studies was substituted by a recently developed k-optimality criterion that allowed for simultaneous optimization of a large number of objectives, leading to a significant improvement over the results of earlier studies. A large number of optimum charging strategies were identified through this procedure and thoroughly analyzed, in view of an efficient blast furnace operation.

Original languageUndefined/Unknown
Pages (from-to)1179–1188
JournalMaterials and Manufacturing Processes
Volume32
Issue number10
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

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