Ironmaking process under artificial intelligence technology: A review

The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.