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

Tamoghna Mitra; Frank Pettersson; Henrik Saxén; Nirupam Chakraborti

doi:10.1080/10426914.2016.1257133

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

Tamoghna Mitra, Frank Pettersson, Henrik Saxén, Nirupam Chakraborti

Research output: Contribution to journal › Article › Scientific › peer-review

19 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 language	Undefined/Unknown
Pages (from-to)	1179–1188
Journal	Materials and Manufacturing Processes
Volume	32
Issue number	10
DOIs	https://doi.org/10.1080/10426914.2016.1257133
Publication status	Published - 2017
MoE publication type	A1 Journal article-refereed

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10.1080/10426914.2016.1257133

http://www.tandfonline.com/doi/abs/10.1080/10426914.2016.1257133

Cite this

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title = "Blast furnace charging optimization using multi-objective evolutionary and genetic algorithms",

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.",

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AB - 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.

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DO - 10.1080/10426914.2016.1257133

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