Study of Low Flow Rate Ladle Bottom Gas Stirring Using Triaxial Vibration Signals

• Published in: Metallurgical and materials transactions. B, Process metallurgy and materials processing science Vol. 49; no. 1; pp. 423 - 433
• Main Authors: Yenus, Jaefer, Brooks, Geoffrey, Dunn, Michelle, Li, Zushu, Goodwin, Tim
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2018; Springer Nature B.V
• Subjects: BUBBLES; Bubbling; Characterization and Evaluation of Materials; Chemistry and Materials Science; DISTURBANCES; ENGINEERING; FLOW RATE; Flow velocity; FREQUENCY RANGE; Frequency ranges; GAS FLOW; Ladle metallurgy; Ladles; LEAST SQUARE FIT; MATERIALS SCIENCE; Mathematical models; Metallic Materials; Metallurgy; METALS; MONITORING; Nanotechnology; Rare gases; Steel making; Steel products; STEELS; STIRRING; Structural Materials; SURFACES; Surfaces and Interfaces; Thin Films; Vibration; WALLS; Plug Life; Cold Model; Stirring Power; Ladle Operators; Recirculation Speed
• ISSN: 1073-5615; 1543-1916
• DOI: 10.1007/s11663-017-1118-2

Secondary steelmaking plays a great role in enhancing the quality of the final steel product. The metal quality is a function of metal bath stirring in ladles. The metal bath is often stirred by an inert gas to achieve maximum compositional and thermal uniformity throughout the melt. Ladle operators often observe the top surface phenomena, such as level of meniscus disturbance, to evaluate the status of stirring. However, this type of monitoring has significant limitations in assessing the process accurately especially at low gas flow rate bubbling. The present study investigates stirring phenomena using ladle wall triaxial vibration at a low flow rate on a steel-made laboratory model and plant scale for the case of the vacuum tank degasser. Cold model and plant data were successfully modeled by partial least-squares regression to predict the amount of stirring. In the cold model, it was found that the combined vibration signal could predict the stirring power and recirculation speed effectively in specific frequency ranges. Plant trials also revealed that there is a high structure in each data set and in the same frequency ranges at the water model. In the case of industrial data, the degree of linear relationship was strong for data taken from a single heat.