基于遗传算法的板坯连铸凝固模型表面喷雾冷却优化研究  

Study of Spray Cooling in Process of Continuous Slab Casting Based on Genetic Algorithm

作　　者：王海霞[1] 王军[2] 

机构地区：[1]长春职业技术学院工程技术分院,长春130000 [2]长春大学材料工程学院,长春130000

出　　处：《表面技术》2016年第2期194-198,共5页Surface Technology

基　　金：国家自然科学基金(50906102)~~

摘　　要：目的对连续铸造过程中凝固模型表面喷雾冷却进行优化,实现表面温度的最优冷却。方法分析板坯的连铸工艺过程,推导板坯连铸的凝固模型,获得热传递方程式,对冷却过程的边界条件进行约束。对凝固模型进行仿真验证,与基准温度变化曲线进行比较。结合具体实例确定冷却过程中需要优化的目标对象,采取遗传算法搜索最优解,对相关参数进行仿真。结果和优化前相比,优化后板坯凝固过程中单位时间内温度变化波动较小,水流量密度最大值约为35 L/(m^2·s)。结论优化后的板坯连铸过程中单位时间内喷雾冷却温度变化均匀,喷水量较少,能避免板坯连铸凝固模型表面裂纹的产生,提高产品的质量,效果较好。Objective To optimize spray cooling of solidification model surface in the process of continuous casting so as to realize the optimal cooling surface temperature. Methods The analysis of the continuous slab casting process was conducted,the solidification model of continuous slab casting was deduced and the heat transfer equations were given to constraint boundary conditions of cooling process. The model of solidification simulation was verified and compared with the reference temperature change curve. In combination with a practical example,the target object needed to be optimized was determined during the cooling process. The genetic algorithm was adopted to search the optimal solution,and the relevant parameters were simulated. Results Compared with before simulation,the temperature fluctuation per unit time was small during the solidification process of slab after optimization and the maximum water flow density was about 35 L /（ m^2·s）. Conclusion The fluctuation of spray cooling temperature was evenly in the optimized process of continuous slab casting in unit time and the water quantity was less,avoiding cracks on the surface of slab caster solidification model and improving the quality of the products,which achieved better effects.

关 键 词：凝固模型 优化 遗传算法 板坯连铸 冷却 温度 仿真 

分 类 号：TF777[冶金工程—钢铁冶金]