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

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Tamoghna Mitra, Frank Pettersson, Henrik Saxén, Nirupam Chakraborti
Publisher: Taylor & Francis
Publication year: 2017
Journal: Materials and Manufacturing Processes
Journal acronym: Mater. Manuf. Processes
Volume number: 32
Issue number: 10
Start page: 1179
End page: 1188
eISSN: 1532-2475


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.


Last updated on 2019-07-12 at 03:09