Online Supervisory System for In-Process Optimization of Calcium Additions by Continuously Monitoring the State of Non-metallic Inclusions Inside Low-Alloyed Liquid Steels

Abstract

A decision support system was developed using supervised machine learning (ML) approach for optimization of calcium (Ca) additions by continuously monitoring the physical state of non-metallic inclusions (NMIs) inside low-alloyed liquid steels. In this work, two instances were considered to design the base algorithm for the proposed supervisory system: (1) Clogging of submerged entry nozzle (SEN) during continuous casting of steels due to accumulation of solid oxide non-metallic inclusions (NMIs) and (2) Ca treatment during secondary steelmaking for modification of oxide NMIs from solid to liquid state to avoid SEN clogging. At first, experimental investigations were carried out on liquid steel samples from three low-alloyed Ca-treated steel grades from the same steel family to evaluate the characteristics of solid oxide NMIs that cause SEN clogging. In the next step, data-driven models were developed using an in-house ML algorithm trained primarily with process data for calculating the value of the newly proposed dummy parameter ‘Clog.’ These models, after testing, were architected to develop a supervisory system based on experimental investigations and data-driven models. The objective of this proposed supervisory system was to predict the optimum quantity of Ca needed for successful modification of NMIs from solid to liquid state to avoid SEN clogging based on the forecasted ‘Clog’ value. Finally, industrial data from ~ 3000 heats were tested to verify the results obtained from the developed supervisory system. The results confirmed that this novel supervisory system could predict the optimum class of Ca for all studied steel grades with 95 to 98 pct accuracy. The integration of this online supervisory system in steel production is expected to minimize operators’ corrective actions in achieving realistic control of Ca additions.