**6. Conclusions**

We applied a machine learning-based analysis to a dataset (operating and output data) from a pilot basic oxygen steelmaking (BOS) converter. Correlation analysis showed:


The method is easily scalable for industrial applications. In addition, the machine-learning model does not simplify the problem and is able to predict the decarburization rate accurately through learning from the real dataset acquired from BOS pilot plants.

**Author Contributions:** A.R. and S.S. wrote the machine-learning algorithms and produced the figures. A.R. and S.S. discussed the machine-learning results. Z.L. provided and prepared the datasets. A.R., S.S. and Z.L. discussed the materials case study. All authors wrote and commented on the manuscript and figures. All authors have read and agreed to the published version of the manuscript.

**Funding:** Z.L. would like to thank the funding from EPSRC (Engineering and Physical Sciences Research Council, UK) under grant number EP/N011368/1(EPSRC Fellowship).

**Acknowledgments:** The authors are grateful to Johan Eriksson at Swerea MEFOS for the interpretation of the information of the trials carried out in a 6-ton pilot converter at the MEFOS in Sweden. The authors also appreciate Chris Barnes at Tata Steel, UK, who provided the actual industry BOS converter data for the validation of the work.

**Conflicts of Interest:** The authors declare no conflicts of interest.
