The power generation industry heavily uses simulation to evaluate what-if scenarios for process improvement and control logic testing. Simulation typically lives in the design and planning phase; due to computational lag, these insights cannot be produced quickly enough to inform real-time control.

To show how high-performance machine learning (ML) models can provide simulation-quality insight in realtime, Geminus created a fast, accurate surrogate of a high-fidelity simulation of a natural gas combined cycle (NGCC) power plant. Download the full case study to learn more.