Steel Manufacturing Process Aspects TRIZ Cases
- Blast Furnace Thermal Control Using Reinforcement Learning
Reinforcement learning combines multi-furnace history with simulated thermal states to cut fuel use, stabilize iron quality, and extend furnace life.
- Blast Furnace State Classification for Faster Smelting Diagnosis
Machine-learned classification of blast furnace states turns key process parameters into early smelting deterioration diagnosis.
- Production Facility Monitoring for Centralized Abnormality Detection
Centralized analysis of current and historical plant data improves abnormality detection across multiple production sites with fewer operators.
- Blast Furnace Fault Detection Using Ventilation and Sensor Indices
Separates blast furnace operational faults from shaft-pressure sensor faults by combining a fault index with a ventilation index for earlier detection.
- LF Molten Steel Temperature Control With Interpretable ML
SHAP-guided temperature prediction makes ladle furnace refining more transparent, helping stabilize molten steel temperature and process settings.
- Blast Furnace Temperature Control Using Image-Based Learning Models
Image-based machine learning turns blast furnace history data into operator guidance that keeps molten iron temperature accurate under disturbances.
- Blast Furnace Fault Detection Using Sensor Synchronization
Separate fault and ventilation indices expose shaft-pressure sensor faults early while distinguishing them from true blast furnace abnormalities.