基于人工蜂群算法的连铸二冷多目标优化研究  

Study on Multi-objectives Optimization of Secondary Cooling of Continuous Casting Based on Artificial Bee Colony Algorithm

作　　者：龙艳彬[1] 陈雪波[2] 高鉴文 吴文波[1] 

机构地区：[1]辽宁科技大学电子与信息工程学院,辽宁鞍山114051 [2]辽宁科技大学研究生院,辽宁鞍山114051

出　　处：《热加工工艺》2017年第11期85-89,92,共6页Hot Working Technology

摘　　要：构建二维板坯瞬态传热数学模型,根据冶金准则及设备约束条件设计连铸二冷优化多目标函数,并从聚类准确度和收敛性角度考虑,引入改进的人工蜂群算法,以目标函数最小化为准则,对二冷各段水量进行优化。经实验证明,优化后的二冷水配比使得铸坯的表面温度增加104℃,铸坯的质量缺陷平均级别降低,缺陷铸坯占总量百分比下降。A mathematical model of transient heat transfer for 2D slab was established, and multi-objective function for optimization of secondary cooling of continuous casting was designed according to the metallurgical criteria and equipment constraint. From the view of the accuracy and convergence of the cluster, the improved artificial bee colony algorithm was introduced, then taking the minimum of the objective function as criterion, the optimization of water ratio during secondary cooling process was implemented. The experimental results show that the optimized water ratio during secondary cooling makes the slab surface temperature increase by 104 ℃, the degree of the slab defects and the total percentage of defective slab decrease.

关 键 词：连铸 人工蜂群算法 二冷配水 多目标优化 

分 类 号：TG249.7[金属学及工艺—铸造] TF777.1[冶金工程—钢铁冶金]