AI Aluminum Smelting Energy
Machine learning for optimizing energy consumption in aluminum electrolysis and smelting operations
The Problem
“Cut aluminum smelter power cost and volatility”
Organizations face these key challenges:
Electricity price volatility and demand charges drive large, hard-to-control cost swings for a continuous, inflexible load
Potline instability (anode effects, voltage noise, feeder malfunctions) increases energy intensity, reduces current efficiency, and risks metal quality
Siloed operations and energy trading decisions limit the ability to safely monetize flexibility (curtailment, ramping) without production risk
Impact When Solved
The Shift
Real-World Use Cases
AI in Energy Industry: Smart Grid Optimization and Energy Management
This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.
Artificial Intelligence for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.