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To enhance energy efficiencyEnergy efficiency, potline performance, and safetySafety, advanced Industry 4.0 solutions were implemented in an Industrial Aluminium smelterAluminium smelter. These solutions are based on the real-time data, machine learningMachine learning, and AI to detect anomalies, predict potential faults, and generate timely alerts for certain recurrent issues such as high instabilityInstability and multiple anode effectsAnode Effect (AE). This solution also notifies operators when pot conditions are stable and does not need extra voltage, thereby supporting more efficient process controlProcess control. This paper outlines the data engineering, AI methodology, and field observations, demonstrating how these solutions contribute to improved process reliability, safetySafety, early fault detection, thus enhanced pot performance.

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Enhancing Potline Reliability and Efficiency Through Intelligent Data Automation and Predictive Insights

  • 会议论文

  • 发表日期：

    2026年

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  • Prateek Kumar Lath,
  • Rutuja Gajankush,
  • Anshu Mangal,
  • Kapil Kumar,
  • Anish Das,
  • Himan Kundu,
  • Amit Gupta

摘要

To enhance energy efficiencyEnergy efficiency, potline performance, and safetySafety, advanced Industry 4.0 solutions were implemented in an Industrial Aluminium smelterAluminium smelter. These solutions are based on the real-time data, machine learningMachine learning, and AI to detect anomalies, predict potential faults, and generate timely alerts for certain recurrent issues such as high instabilityInstability and multiple anode effectsAnode Effect (AE). This solution also notifies operators when pot conditions are stable and does not need extra voltage, thereby supporting more efficient process controlProcess control. This paper outlines the data engineering, AI methodology, and field observations, demonstrating how these solutions contribute to improved process reliability, safetySafety, early fault detection, thus enhanced pot performance.