| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Aluminum Reduction Technology
|
| Presentation Title |
ML-Powered Pot Performance Prediction in Aluminium Smelter |
| Author(s) |
Manish Jaiswal, Shanmukh Rajgire, Atanu Maity, Kishor Pattnaik, Philip Hansda, Pramod Shukla, Kazi Arshad Ansari, Pratap Sahu, Debasish Mallik |
| On-Site Speaker (Planned) |
Manish Jaiswal |
| Abstract Scope |
Hirakud Smelter (HKD), a unit of Hindalco Industries Limited is a part of Aditya Birla Group (ABG). Hirakud Aluminium is an integrated aluminium smelting complex which uses GAMI Technology and is one of the oldest smelters in India, established in 1959. The potlines, converted from Søderberg to prebake in 2009, have inherent challenges in terms of technology and retrofitting the old pots to prebakes. Facing rising energy costs, aluminium smelters aim to reduce specific energy consumption by enhancing current efficiency (CE), which lowers energy use and boosts productivity.
This article presents a predictive model using Logistic Regression to identify low-efficiency pots in 235kA aluminium smelting operations. Utilizing data from a 235kA pot-line, the model predicts pot efficiency with high accuracy and precision by analyzing key performance indicators and operational metrics. By providing real-time insights, it pinpoints underperforming pots, enabling targeted interventions that enhance overall process efficiency and productivity. The findings demonstrate the potential of machine learning in optimizing industrial processes and contribute to significant improvements in operational efficiency and resource utilization. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Machine Learning, Other |