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Optimizing Transition Tonnage in Continuous Casting Process with IoT
Technology Category
- Analytics & Modeling - Digital Twin / Simulation
- Sensors - Liquid Detection Sensors
Applicable Industries
- Aerospace
- Consumer Goods
Applicable Functions
- Logistics & Transportation
- Quality Assurance
Use Cases
- Manufacturing Process Simulation
- Virtual Reality
Services
- Testing & Certification
The Challenge
The continuous casting of steel, particularly when casting different grades in the same sequence, produces transition billets. These billets do not conform to any specific grade and thus need to be downgraded or diverted. The challenge lies in identifying the extent and location of this intermixed zone to minimize production and quality issues. The process of billet casting to convert liquid steel to solid billets is fraught with uncertainties and variables. For instance, the casting speed may change or certain strands may become non-functional, altering the flow in the tundish and changing the transition tonnage. Predicting and optimizing the transition tonnage during the grade change under different plant scenarios is a significant challenge. To better understand and manage this process, a CFD model was developed.
About The Customer
Tata Steel is one of the world’s pioneering steel companies, manufacturing a wide range of steel products. The company primarily serves customers in the automotive, construction, consumer goods, engineering, packaging, lifting and excavating, energy and power, aerospace, shipbuilding, rail and defense, and security sectors. Tata Steel has manufacturing operations in 26 countries, including Australia, China, India, the Netherlands, Singapore, Thailand and the United Kingdom.
The Solution
The solution involved the use of ANSYS Fluent software with the realizable k-e turbulence model to model fluid flow in the tundish. The unsteady-state species transport model was used to generate the residence time distribution of the vessel. Further data analysis was carried out to predict the start and end of transition in the tundish. The robust solver and the interactive user interface in ANSYS Fluent helped to speed up the analysis setup and subsequent post-processing. The accuracy of the simulation helped Tata Steel to optimize the transition tonnage produced during the grade change. This quality control measure led to a reduction in customer complaints and saving of transition tonnage. The use of ANSYS Fluent also reduced the cost of plant-scale experiments and testing.
Operational Impact
Quantitative Benefit
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