Case Study: a large steel production company achieves 2% natural gas savings potential with Energiency's machine learning furnace consumption model

Machine Learning modeling for drifts detection of the gas consumption of a roll mill steel furnace

The customer, a large steel production company, sought to reduce the natural gas consumption of its rolling mill furnace. They engaged Energiency, which provides an energy management software platform and dedicated coaching services, to address this challenge.

Energiency analyzed a year's worth of high-frequency operational data to build an AI-based gas consumption model. This solution established a baseline to detect abnormal consumption in real time. The model identified a 2% natural gas savings potential and is now used on Energiency's platform to help furnace operators track performance improvements.