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Artificial IntelligencePilotEnergy optimisation

Energy optimisation — Glass

Reinforcement-learning control of chillers, compressors and furnaces. Delivered for a glass client in Mumbai.

Location
Mumbai
Year
2025
Duration
21 weeks
Team
9 engineers
Interactive 3D · drag to rotate
11%
energy saved
17.9 mo
payback
554
t CO₂/yr avoided

The challenge

Conditions on site — heat, dust, vibration and patchy connectivity — had defeated two earlier attempts at this in glass.

What we built

We ran a two-week discovery sprint, then built and fielded the system end to end on Ray RLlib, Python and BACnet, with operator training and an SLA.

Delivery timeline

  1. Phase 1

    Discovery sprint and site survey

  2. Phase 2

    Prototype built and tested in the Motera lab

  3. Phase 3

    Pilot on site with before/after measurement

  4. Phase 4

    Scale-out, training and handover

Highlights

  • Remote monitoring and over-the-air updates included
  • Runs on-premises; no data leaves the site unless agreed
  • Safety case reviewed with the client's EHS team
  • Operator training delivered in Gujarati, Hindi and English

Have a problem that needs a machine built for it?

Tell us the constraint — line speed, accuracy, environment, deadline. An engineer will read it and reply with a straight answer, including when the answer is that you don't need us.

We reply to every enquiry within one business day.