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

被引：18

作者：

Mitra, Tamoghna ^{[1
]}

Pettersson, Frank ^{[1
]}

Saxen, Henrik ^{[1
]}

Chakraborti, Nirupam ^{[2
]}

机构：

[1] Abo Akad Univ, Thermal & Flow Engn Lab, Fac Sci & Engn, Turku, Finland

[2] Indian Inst Technol, Dept Met & Mat Engn, Kharagpur 7213012, W Bengal, India

来源：

MATERIALS AND MANUFACTURING PROCESSES | 2017年 / 32卷 / 10期

关键词：

Blast furnace; burden distribution; charging; genetic algorithms; multi-objective optimization; BURDEN DISTRIBUTION; MODEL; OPTIMALITY;

D O I：

10.1080/10426914.2016.1257133

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：1179 / 1188

页数：10

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