高炉炼铁智能化发展的研究现状与展望
Research status and prospects of intelligent development of blast furnace ironmaking
刘小杰 1张玉洁 1刘然 1张智峰 1李欣 1陈树军2
作者信息
- 1. 华北理工大学冶金与能源学院,河北省现代冶金技术重点实验室,河北唐山 063210
- 2. 河钢集团有限公司承德分公司,河北承德 067002
- 折叠
摘要
长期以来,传统的高炉炼铁工艺存在着能源消耗高、污染严重等问题.随着"双碳"目标政策的提出,如何利用智能制造技术实现高炉冶炼高效、环保的可持续发展已经成为高炉工作者的研究重点.从高炉炼铁智能化发展现状作为切入点,详细地从智能预测、模拟仿真、专家系统以及互联网平台几个方面进行了阐述.即以高炉炼铁现场数据为基础,分别探讨高炉运行状态预测和模拟仿真在高炉中的应用,为高炉炼铁智能化控制提供决策支持;建立高炉炼铁工业可视化平台,实现高炉炼铁生产过程的全面监控和管理;最后,综合当前研究现状,结合大数据技术和炼铁工艺,从高炉炼铁数据治理、专家知识库的建立、智能化平台的建立等几个角度进一步展望了高炉炼铁智能化的发展.
Abstract
For a long time,traditional blast furnace ironmaking processes have faced problems such as high energy consumption and severe pollution.With the proposal of goals such as"dual carbon",how to use intelligent manufacturing technology to achieve efficient and environmentally friendly sustainable development in blast furnace smelting has become a research focus for blast furnace workers.From the current situation of intelligent development of blast furnace ironmaking as an entry point,several aspects of intelligent prediction,simulation,expert system and internet platform were elaborated in detail.Based on the on-site data of blast furnace ironmaking,the application of blast furnace operation state prediction and simulation in blast furnace were explored,providing decision support for intelligent control of blast furnace ironmaking;a visualization platform for the blast furnace ironmaking industry was established to achieve comprehensive monitoring and management of the blast furnace ironmaking production process.Finally,based on the current research status,combined with big data technology and ironmaking processes,the development of intelligent ironmaking in blast furnaces was further discussed from several perspectives,including data governance,establishment of expert knowledge bases,and establishment of intelligent platforms.
关键词
高炉炼铁/数据驱动/人工智能/大数据/智能化Key words
blast furnace ironmaking/data-driven/artificial intelligence/big data/intelligence引用本文复制引用
基金项目
国家自然科学基金青年基金(52004096)
出版年
2024
NETL
NSTL
万方数据