摘要
针对连铸过程中影响铸坯内部质量的二冷区冷却水量的确定,提出一种基于粒子群优化水量的新方法.首先应用凝固原理和控制容积方法建立凝固过程数学模型,运用射钉硫印坯壳测厚法对模型进行验证.将冷却过程中的矫直温度,以及在各冷却段的温升温降控制限等作为优化目标,运用粒子群优化人工智能方法优化连铸过程二冷区冷却水量,获得拉速-水量的优化关系曲线.现场实际运用于Q235钢,结果显示,中心裂纹由1.5级降为0.5级,缩孔缺陷降低了7.2%,裂纹缺陷降低了3.6%,获得良好的效果.
To determine the water consumption in the secondary cooling zone during continuous casting, which will affect the internal quality of CC billets, a new approach is proposed, ed to it on the basis of particle swarm optimizer （PSO）. A mathematical model of solidification process is thus developed according to the principle of solidification with volume controlled, and the model is verified by measuring billet shell thickness through nail shooting and sulfur printing. Then, the flattening temperature, reheating temperature and its rise/drop limits are taken as objectives in the artificial intelligent PSO to optimize the water consumption in the secondary ceoling zone during continuous casting. Applying the model to the steel Q235, the results showed that the core crack is improved to Grade 0.5 from 1.5 with porosity rate reduced by 7.2 % and cracks reduced by 3.6 %. It shows the effectiveness of the model.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第12期1134-1137,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(50174021)
关键词
连铸
二次冷却区
冷却水量
粒子群算法
continuous casting
secondary ceoling zone
ceoling water consumption
particle swarm optimizer （PSO）
