Application of neural network in steelmaking and continuous casting: A review

Abstract

With the improvement of computer computing power and the development of big data technology, neural networks have rapidly developed and been effectively applied in multiple fields. Steelmaking and continuous casting are key stages in steel production, involving complex physical changes and chemical reactions under high-temperature conditions. Neural networks, with the advantage of intelligently mining patterns from massive data, have been extensively researched and applied in the control of steelmaking and continuous casting processes. This paper uses a systematic literature review method to summarise the applications of neural networks in converter steelmaking, electric arc furnace (EAF) steelmaking, secondary refining and continuous casting over the past two decades. The main areas include the prediction of endpoint temperature and composition in converter and EAF smelting, optimisation of EAF power supply parameters, prediction of breakout in continuous casting and predictive control of billet and slab defects in continuous casting. It summarises and analyses the advantages and effects of different types of neural networks applied in steelmaking and continuous casting. Finally, the paper highlights the potential and challenges of neural networks in the application of steelmaking and continuous casting and points out future development directions.