Review paper summarises ML applications for Basic Oxygen Furnace steelmaking
Dr Bogdan Nenchev of Intellegens is a co-author on a new review paper in Materials Genome Engineering Advances which presents the first comprehensive review of machine learning (ML) applications in the BOF steelmaking process. The publication highlights the pivotal role of ML in steering the shift toward intelligent, efficient, and sustainable steel production.
Alchemite™ from Intellegens provides an off-the-shelf commercial software solution that enables steel R&D organisations to quickly apply ML methods such as those covered in the paper. Alchemite™ can also address some of the challenges highlighted in the paper – for example, reducing the need for data pre-processing when training ML models with sparse and noisy experimental or process data.
Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry’s output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, machine learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.
Published in: Materials Genome Engineering Advances e6 (2023)
Title: Toward learning steelmaking – A review on machine learning for the basic oxygen furnace process
Authors: Maryam Khaksar Ghalati, Jianbo Zhang, G. M. A. M. El-Fallah, Bogdan Nenchev, Hongbiao Dong