Explore the full BOS process—equipment, models, and sensors—to master steelmaking control and optimize product quality.
Blow Theory
Full Code
MAN0104220
Module Type
E-learning
Description
Short description:
Master BOS parameter control and sensor-based optimization through advanced modeling and feedback strategies.
Long description:
This advanced module focuses on optimizing the Basic Oxygen Steelmaking (BOS) process by controlling key parameters and integrating real-time sensor feedback. Learners will explore the significance of oxygen blowing, endpoint detection, and slag formation in achieving metallurgical targets. The module details the use of statistical, neural, static, and dynamic models for interpreting sublance and off-gas data, enhancing in-process decisions. Emphasis is placed on model accuracy, material balance, and slag chemistry. Learners will apply analytical strategies to align sensor outputs with operational parameters to maintain consistent steel quality and minimize resource waste.
What you will learn
- Describe the primary BOS process control parameters and their influence on steel quality.
- Explain the function of sublance, off-gas, vibration, and weighing sensors in BOS monitoring.
- Compare static charge, dynamic tuning, and neural network models for BOS decision support.
- Assess the role of sensor-data interpretation in regulating endpoint temperature and carbon levels.
- Apply optimization strategies to minimize slag carryover, improve tapping, and reduce steel impurities.
Module content
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