Abstract Scope |
Steel manufacturing is a long and complicated process including stages of BF ironmaking, BOF steelmaking, refining, casting and rolling; thousands of processing parameters can potentially influence mechanical properties of final products. Recently, significant progress has been made in steel industry to develop online monitoring systems, collecting data for process control. Challenges remain in the area of data storage, cross-process data links, erroneous datasets, the correlation between chemistry, process variables and mechanical properties. 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. 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 of metallurgy process, and feedback into a backpropagation neural network (NN) model. Integrating data mining and machine learning generates reasonable predictions and addresses process efficiencies within the BOF steelmaking process. |