About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Aluminum Reduction Technology
|
| Presentation Title |
Enhancing Potline Reliability and Efficiency through Intelligent Data Automation and Predictive Insights |
| Author(s) |
Prateek Kumar Lath, Amit Gupta, Anish Dash, Himan Kundu, Anshu Mangal, Kapil Kumar, Rutuja Gajankush |
| On-Site Speaker (Planned) |
Prateek Kumar Lath |
| Abstract Scope |
To enhance energy efficiency, potline performance and safety, advanced industry 4.0 solutions was implemented in an industrial Aluminium smelter. These solutions are based on the real-time data, machine learning and AI to detect anomalies, predict potential faults, and generate timely alerts for certain recurrent issues such as high instability, multiple anode effects etc. This solution also notifies operators when pot conditions are stable and does not need extra voltage, thereby supporting more efficient process control. This paper outlines the data engineering, AI methodology, and field observations, demonstrating how these solutions contribute to improved process reliability, safety, early fault detection, thus enhanced pot performance. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Machine Learning, |