基于辅料资源运行特性的高炉综合焦比参数优化研究  

Optimization research of the comprehensive coke rate of blast furnace based on operational characteristic of auxiliary materials

作　　者：王艳红[1] 张华[2] 冯朝辉[2] 赵刚[2] 何志朋[2] 

机构地区：[1]武汉科技大学管理学院,湖北武汉430081 [2]武汉科技大学

出　　处：《冶金自动化》2013年第1期12-15,共4页Metallurgical Industry Automation

基　　金：国家自然科学基金项目(70971102);教育部人文社会科学研究项目(09YJC630182);湖北省高等学校优秀中青年团队计划项目(T201102)

摘　　要：高炉炼铁系统的辅料资源在配合含铁主料资源的转换过程中,加入方式、设备状态、操作参数等决定了不同的高炉综合焦比。本文在对高炉生产工艺中各辅料资源运行特性进行深入研究和分析的基础上,找出影响高炉综合焦比的主要因素,并采用BP神经网络模型拟合综合焦比与主要影响因素间的映射关系。在此基础上以综合焦比最低为目标,以某钢铁公司6#高炉实际生产数据为依据,采用遗传算法对综合焦比的主要影响因素进行参数优化。结果表明,该方法对降低综合焦比科学、有效。While the ironmaking accessories resources of the blast furnace working in with the conver- sion process of iron main ingredient resources, the blast furnace comprehensive coke rate was deter- mined by the added way of the accessories resources, device status and operating parameters. In this paper, we analyzed the resource operating characteristics of various accessories in the blast furnace pro- duction process to identify the main factors affecting the integrated coke rate and used BP neural net- work model to simulate the mathematic mapping relationship between comprehensive coke rate and the main influencing factors. On this basis, a genetic algorithm method was adopted to find the optimal combination of main influencing factors, with the goal to minimize the integrated focal ratio. Using No. 6 blast furnace actual production data of a steel company as an example, the results show the method to be more effective to reduce overall focal ratio.

关 键 词：高炉 辅料资源 运行特性 综合焦比 遗传算法 神经网络 

分 类 号：TF54[冶金工程—钢铁冶金]