AI-driven refinery process optimization, built by process engineers.
Twin Process AI was started by M.Tech graduates from IIT Madras with backgrounds in chemical engineering and machine learning. We combine academic depth with real-world industrial exposure to turn plant data into decisions a process engineer can trust.
Our mission
To empower energy companies with AI-driven solutions that deliver measurable business value through smarter, more efficient and sustainable operations.
The problem
Refinery operations face growing data and efficiency gaps: wrong or dead instrument tags, DCS and lab data that rarely reconcile, heat and material balances that never close, utility losses that go undetected, and optimization studies that take days per case in spreadsheets. These gaps compound, costing energy, throughput and reliability every day.
An end-to-end, AI-enabled workflow.
A closed-loop pipeline, from raw plant data to actionable, self-tuning recommendations.
- 01
Plant data
- 02
Data validation
- 03
Heat and material balance
- 04
Tag normalization
- 05
Simulation
- 06
Optimization
- 07
AI recommendation engine
- 08
Continuous monitoring
How we work
The balance comes first. Every downstream calculation rests on a fully closed mass and energy balance, so we validate tags against the P&ID, flag drifted or frozen instruments, and reconcile flows, temperatures and pressures before any model is allowed to read them.
Physics, then AI. First-principles simulation describes the equipment; machine learning sits on top to make it fast and to infer what the instruments miss. Not the other way round.
We do not claim plant results we do not have. Every number on this site comes from a first-principles simulation. Calibration against a live heater is our next milestone.
Where this goes
From insight to autonomous action.
AI copilot
For every process engineer, every shift.
Digital twin integration
A live virtual replica of plant units.
Autonomous optimization
Self-adjusting operating windows.
Predictive simulation
Forecast performance before it happens.
Explainable AI
Transparent, auditable recommendations.
Closed-loop optimization
Recommendations feed back automatically.
Enterprise-wide deployment
Scaling across sites and units.
Key takeaways
Reliable data. Smarter simulation. Autonomous optimization.
- A validated heat and material balance is the trust layer for every decision.
- Simulation plus AI rules turn plant data into repeatable, tunable operating logic.
- A phased MVP delivers value at every stage, toward a full AI copilot.
We are pre-pilot and looking for a first unit to calibrate against. If you run one, you get the analysis done properly, and we will tell you where our model disagrees with your plant rather than quietly tuning until it agrees.