Sado, S.; Jastrzębska, I.; Zelik, W.; Szczerba, J. Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review. Materials2023, 16, 7396.
Sado, S.; Jastrzębska, I.; Zelik, W.; Szczerba, J. Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review. Materials 2023, 16, 7396.
Sado, S.; Jastrzębska, I.; Zelik, W.; Szczerba, J. Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review. Materials2023, 16, 7396.
Sado, S.; Jastrzębska, I.; Zelik, W.; Szczerba, J. Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review. Materials 2023, 16, 7396.
Abstract
Nowadays, digitalization and automation in both industrial and research activities is a driving force for innovations. In recent years, Machine Learning (ML) techniques - the subset of Artificial Intelligence - have been widely applied as a key tool to automize and digitalize numerous in-dustrial processes. ML models can predict specific features, like materials' lifetime in a heating device, based on recently trained algorithms with applied input data. The results of ML algorithms are easy to interpret and can significantly decrease the time of research and decision-making, thus, substituting the trial-error approach. This work presents the state of the art in the application of Machine Learning in the investigation of MgO-C refractories for the steel industry. Also, the work presents an overview of the most commonly used ML algorithms in refractory engineering. So far published results confirm the high performance of ML, e.g., for prediction of oxidation behaviour, optimum graphite content and wear rate of MgO-C refractories, or for clustering materials based on various fused and sintered MgO sources into groups with similar corrosion resistance, thus, permitting to reduce expensive fused raw materials content. Most of the presented works were positively validated via experiment.
Keywords
machine learning; MgO-C; refractories
Subject
Engineering, Metallurgy and Metallurgical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
