Metallurgical Data Science for Steel Industry: A Case Study on Basic Oxygen Furnace
Bogdan Nenchev, Chinnapat Panwisawas, Xiaoan Yang, Jun Fu, Zihui Dong, Qing Tao, Jean‐Christophe Gebelin, Andrew Dunsmore, Hongbiao Dong, Ming Li, Biao Tao, Fucun Li, Jintong Ru, Fang Wang
Abstract
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.