Field Implementation of an Expert System for Energy Efficiency Improvement in Industrial Aluminum Electrolysis: A Rule-Based Explainable AI Approach

This study reports a field-validated expert system for 230 kA aluminum reduction cells at Chalco Guizhou Branch, achieving sustained energy savings of 137 kWh/t (annual comprehensive benefits of 333,714 CNY/cell) over six consecutive months. Addressing “black-box” AI limitations, the system employs hybrid knowledge representation (production rules, frame-based structures, certainty factors) within an XAI framework. The four-layer architecture integrates OPC UA/Modbus TCP protocols for real-time data acquisition and interpretable diagnosis. Field trials demonstrated 94.2% diagnostic accuracy, significantly outperforming manual diagnosis (87.6%, p < 0.001) while achieving comparable performance to LSTM deep learning (93.8%, p = 0.42), with 15× faster inference speed (3.5 s vs. 52 s). Industrial implementation increased current efficiency by 0.7%, reduced DC power consumption by 137 kW·h/t, and decreased anode effect frequency by 32.5%. The system’s explicit reasoning capability provides transparent diagnostic explanations, bridging the gap between data-driven AI and domain expertise for trustworthy intelligent diagnostics in energy-intensive industrial processes.