Digital process control and superheat prediction in continuous casting

Abstract

The steel industry faces multifaceted challenges such as increasing cost pressures, energy efficiency requirements, sustainability targets, and the need to maintain high-quality standards. Digitalization emerges as an effective solution to overcome these challenges by transforming traditional production processes. Supported by advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT), digitalization enables unmanned and highly automated production environments, contributing to process optimization and enhanced efficiency. This article focuses on the importance of dynamic process monitoring in the tundish area and examines the real-time tracking of critical parameters such as temperature, superheat and tundish level control using the CasTemp online temperature measurement system and the CasTip liquidus measurement sensor. Field studies were conducted at the Bilecik Demir Çelik plant, which is equipped with three 30-ton induction furnaces, a ladle furnace, and a three-strand continuous casting machine. In the tundish, which transfers liquid steel from the ladle to the molds, dynamic temperature and liquidus measurements were performed using Heraeus Electro-Nite’s patented CasTemp online temperature measurement system and CasTip liquidus sensor. For heats of the same grade, liquidus and superheat values correlated with carbon analyses of tundish steel samples were monitored, and the effects of observed variations on the process were evaluated. Dynamic process control was achieved using CasTip and CasTemp systems, and experimental studies were carried out on the superheat prediction algorithm.