基于M-NSGA-Ⅱ的转炉炼钢补加供料量优化  

Optimization of Supplementary Feeding for Converter Steelmaking Based on M-NSGA-II

作　　者：徐惟罡 周洪涛[1] XU Weigang;ZHOU Hongtao(School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学人工智能与自动化学院,湖北武汉430074

出　　处：《控制工程》2023年第12期2166-2172,共7页Control Engineering of China

摘　　要：在转炉炼钢过程中,供料量的多少会极大影响钢水的终点碳含量和终点温度,因此,优化转炉炼钢的补加供料量可以提高钢铁的产出质量。首先,依据二次吹炼终点碳温与补加供料量的回归模型构建了补加供料量优化模型;其次,由于转炉炼钢要求优化算法的计算时间短,设计了移动非支配排序遗传算法Ⅱ(movingnon-dominatedsortinggenetic algorithm Ⅱ, M-NSGA-Ⅱ);最后,采用目标函数平均缩小值与平均标准差作为评价指标,对模型结果进行对比实验。结果表明,所设计的M-NSGA-Ⅱ可以在较短的计算时间内获得更优的非支配解集,并且基于该算法的补加供料量优化模型可以有效调整终点碳温。In converter steelmaking process,the end carbon content and end temperature of molten steel are largely determined by the amount of converter steelmaking feed,so the optimization of converter steelmaking feed can improve the quality of steel output.Firstly,the optimization model of feeding is built according to the regression model between the carbon temperature at the end point of secondary blowing and the feeding quantity,and then a M-NSGA-II is designed for the require of short calculation time of algorithm.Finally,results are compared by using the average reduced value of the objective function and the average standard deviation as the evaluation index.The results show that the designed M-NSGA-II can obtain a better non dominated solution set in a shorter calculation time,and the optimization model of supplementary feeding based on the algorithm can effectively adjust the terminal carbon and temperature.

关 键 词：转炉炼钢 M-NSGA-Ⅱ 补加供料量优化 终点碳温 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]