Nowadays, the steel industry is seeking to reduce its carbon footprint without affecting productivity or profitability. This challenge needs to be supported by continuous improvements in equipment, methods, sensors and models. The present work exposes how the combined development of processes and models (CDPM) has been applied to the improvement of hot metal temperature determination. The synergies that arise when both sides of this research are simultaneously approached are evidenced. A workflow that takes into account the CDPM approach is proposed. First, a thermal model of the process is developed, making it possible to identify that hot metal temperature is a key lever for carbon footprint reduction. Then, three main alternatives for hot metal temperature determination are compared: infrared thermometry, time-series forecasting and machine learning prediction. Despite considering only few process variables, machine learning techniques succeed in extracting relevant information from process databases. An accuracy close to infrared thermometry is obtained, with a much higher applicability. This research shows that process-model alternatives are complementary when judiciously nested in the process computer routines. Combining measurement and modelling techniques, 100% applicability is achieved with an error reduction of 7 °C.
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