期刊
STEEL RESEARCH INTERNATIONAL
卷 93, 期 12, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/srin.202100813
关键词
basic oxygen furnace; digitalization; machine learning; metallurgical data; steelmaking
资金
- NISCO UK Research Centre at School of Engineering, University of Leicester
The steel industry utilizes sensors and artificial intelligence to process large amounts of data, optimizing process-performance and predicting product quality. This contributes to the digitalization of the steel industry.
The steel industry has developed sensorization to generate data, monitoring systems, and steelmaking process control. The remaining challenges are data storage issues, lack of cross-production data links, and erroneous datasets, which significantly increase the quality control complexity. The development of a data-driven approach through artificial intelligence (AI) techniques enables machine learning techniques to big datasets aiming to provide process-property optimization and identify challenges and gaps in the data. Recently, computational capabilities and algorithmic developments have significantly grown in power and complexity, accelerating process optimization. Addressing large-scale industrial data process-property optimization strategies involve numerous influencing possessing factors but limited data. As one of the largest production chains in the world, the steel industry faces an ever-increasing demand for larger components, high levels of functionality, and quality of the final product. Herein, an integrated data-driven steelmaking case study is built with the aim of predicting and optimizing the final product composition and quality. Machine learning is used collaboratively with fundamental knowledge, first-principal calculation, and feedback into a backpropagation neural network (NN) model. Integrating data mining and machine learning generates reasonable predictions and addresses process efficiencies within the steelmaking furnaces. The ultimate goal is to enhance the digitalization of the steel industry.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据