A guided analytics tool for feature selection in steel manufacturing with an application to blast furnace top gas efficiency
A guided analytics tool for feature selection in steel manufacturing with an application to blast furnace top gas efficiency
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DOI：
10.1016/j.commatsci.2020.110053
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发表时间：
2021-01
影响因子：
3.3
通讯作者：
Stefan Stein;Chenlei Leng;S. Thornton;Michel F. Randrianandrasana
中科院分区：
文献类型：
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作者：
Stefan Stein;Chenlei Leng;S. Thornton;Michel F. Randrianandrasana
在钢铁制造等知识密集型行业，应用数据分析来优化过程性能，需要领域专家和数据科学家之间有效的知识转移。这通常是一条低效的路径，需要多次迭代，而且从长远来看，在组织知识获取方面不是最优的。通过‘用于参数测试和控制带提取的初始引导分析(IGATE)’工具，我们创建了一个特征选择框架，该框架可以找到有影响的过程参数及其最佳控制带，并且可以很容易地以引导分析工具的形式提供给过程操作员，同时允许他们根据自己的专业知识修改分析。该方法被嵌入到工作流中，由此由领域专家验证提取的参数和控制带，并自动生成分析报告。该方法允许我们将适当的统计分析的能力与过程专业知识相结合，同时显著减少进行特征选择所需的时间。我们将这一应用程序视为在引入更自主的分析方法之前获得用户信心的垫脚石。我们介绍了IGATE的统计基础，并以塔塔钢铁高炉数据的案例研究的形式说明了它的有效性。我们已经在R编程语言的包中免费提供了iGATE核心功能。
In knowledge intensive industries such as steel manufacturing, application of data analytics to optimise process performance, requires effective knowledge transfer between domain experts and data scientists. This is often an inefficient path to follow, requiring much iteration whilst being suboptimal with regard to organisational knowledge capture for the long term. With the ‘initial Guided Analytics for parameter Testing and controlband Extraction (iGATE)’ tool we created a feature selection framework that finds influential process parameters and their optimal control bands and which can easily be made available to process operators in the form of guided analytics tool, while allowing them to modify the analysis according to their expertise. The method is embedded in a work flow whereby the extracted parameters and control bands are verified by the domain expert and a report of the analysis is automatically generated. The approach allows us to combine the power of suitable statistical analysis with process-expertise, whilst dramatically reducing the time needed for conducting the feature selection. We regard this application as a stepping stone to gain user confidence in advance of introduction of more autonomous analytics approaches. We present the statistical foundations of iGATE and illustrate its effectiveness in the form of a case study of Tata Steel blast furnace data. We have made the iGATE core functionality freely available in theigatepackage for the R programming language.