基于Python的球团生产数据可视化研究  

Research on Pellet Production Data Visualization Based on Python

作　　者：刘慧芬 王振阳[2] Liu Hui-fen;Wang Zhen-yang(Information and Automation Department,Tangshan Iron and Steel Company,Hebei Iron and Steel Group,Tangshan 063000,Hebei Province,China;School of Metallurgical and Ecological Engineering,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]河钢集团唐钢公司信息自动化部,河北唐山063000 [2]北京科技大学冶金与生态工程学院,北京100083

出　　处：《科学与信息化》2025年第6期101-103,107,共4页Technology and Information

摘　　要：依托河钢唐钢新区大型球团带式焙烧机生产线,通过配矿结构调整、化学成分变化、热工制度优化、冶金性能检测等手段,自主探索球团各相关生产参数对球团性能的影响,生成了大量的球团生产数据。数据种类及数据量繁多,表格的数据堆放、生产与实验数据的分裂、信息不统一等问题导致球团数据的真正价值无法得到深度挖掘。本文针对这一情况,充分利用Python的可视化工具包,对生产过程中产生的数据进行分析,生成可视化图表,从而提供了更加直观的指引,便于技术人员第一时间对球团生产过程和性能的波动做出调整,指导实际生产。Relying on the large-scale pelletizing belt sintering machine production line in the new district of Tangshan Iron and Steel,this study explores the impact of various production parameters on pellet performance through ore blending adjustments,chemical composition changes,optimization of thermal systems,and metallurgical performance testing.These efforts have generated a large amount of pellet production data.However,the diversity and volume of data,combined with issues such as fragmented data storage,separation of production and experimental data,and lack of data uniformity,have hindered the in-depth mining of the true value of pellet data.In response to this situation,this paper fully utilizes Python’s visualization toolkits to analyze the data generated during the production process and create visual charts.This approach provides more intuitive guidance,enabling technical personnel to quickly adjust the pellet production process and performance fluctuations and to guide actual production.

关 键 词：球团 性能 数据 PYTHON 可视化 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]