Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-dr...Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.展开更多
文章以Web of Science TM核心集中图书情报学科的"大数据""数据驱动"文献为数据源,分析大数据驱动下图书情报学科研究的现状和进展;借助SATI和SPSS软件对507篇文献的关键词进行共词分析和聚类分析。研究表明:大数...文章以Web of Science TM核心集中图书情报学科的"大数据""数据驱动"文献为数据源,分析大数据驱动下图书情报学科研究的现状和进展;借助SATI和SPSS软件对507篇文献的关键词进行共词分析和聚类分析。研究表明:大数据驱动下的图书情报学科研究热点主题主要集中在数字图书馆知识组织与语义互联、社会网络大数据、科研大数据管理与共享、云计算与信息安全、政府数据开放与共享、大数据驱动的知识发现、E-learning与高等教育、数据挖掘与数字人文等方面。展开更多
基金supported in part by the National Natural Science Foundation of China(61933015,61703371,62273030)the Central University Basic Research Fund of China(K20200002)(for NGICS Platform,Zhejiang University)the Social Development Project of Zhejiang Provincial Public Technology Research(LGF19F030004,LGG21F030015).
文摘Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.
文摘文章以Web of Science TM核心集中图书情报学科的"大数据""数据驱动"文献为数据源,分析大数据驱动下图书情报学科研究的现状和进展;借助SATI和SPSS软件对507篇文献的关键词进行共词分析和聚类分析。研究表明:大数据驱动下的图书情报学科研究热点主题主要集中在数字图书馆知识组织与语义互联、社会网络大数据、科研大数据管理与共享、云计算与信息安全、政府数据开放与共享、大数据驱动的知识发现、E-learning与高等教育、数据挖掘与数字人文等方面。
