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.