Energy Optimisation with AI in Process Industries
For a steel, refractory or paint plant, energy is the second-largest operating cost after labour. A 6% reduction on a ₹40 crore annual energy bill is ₹2.4 crore straight to the bottom line. AI-driven energy optimisation is one of the few places where the maths is genuinely compelling.
The Approach Stack
- Model-Predictive Control (MPC): classic control engineering enhanced with learned process models.
- Reinforcement Learning (RL): agents that learn optimal setpoints from simulated and real-world experience.
- Hybrid digital twins: simulator-trained agents validated on real data before deployment.
Where AI Wins
- Combustion control on furnaces and boilers — air-to-fuel ratio optimisation in real time.
- HVAC and compressed-air optimisation — demand-driven setpoints.
- Crystallisation, drying and grinding control — minimising overprocessing energy.
- Power factor and load-balancing across captive power.
Risk Management
Energy optimisation models touch live setpoints. Risk management is not optional:
- All AI setpoint suggestions go through a deterministic safety envelope.
- Operator can override at any moment.
- Continuous evaluation against a "shadow controller" that documents counterfactual energy use.
- Shut-down path is single-button, well-rehearsed.
Practitioner note
Energy AI is one of the highest-leverage AI use-cases in Indian process industries. It is also one of the easiest to do badly. Deploy with a partner who understands both the chemistry and the maths.
Frequently asked
Is this just better SCADA?
No — SCADA shows you the energy. AI-driven optimisation changes the setpoints that consume the energy, within safety constraints.
Amey Kadle
Founder & CEO, Ajinkya Technologies. 20+ years of building MES, ERP and AI systems for India’s most demanding manufacturing plants.