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

被引：16
|
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
收藏
页码：1179 / 1188
页数：10
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