Experiment on the twin, not the plant.
Real-time digital replicas of your process — for what-if simulation, predictive insight and operator training, all on the same validated model.
How Digital Twin works for your plant.
A digital twin mirrors a unit or plant in software, synchronised with live plant data. Run what-if scenarios without touching the process, look ahead with predictive models, and train operators on realistic behaviour — all on the same validated model.
Fork the future. Leave the plant alone.
Left is the plant, right is the twin, synchronised. Start a what-if on the twin — more feed, a fouled exchanger — and it runs ahead to show the consequence while the plant carries on untouched. Let the twin drift and reconcile it against the plant.
- Twin tracks the plant from live data
- What-if runs ahead without touching the process
- Drift is measured, then reconciled
Process model built from engineering data and historians.
Live synchronisation via ComGate / OPC UA.
Twin behaviour checked against plant history.
Scenarios, predictions and training sessions.
Four layers, one truth.
Sensors, historians and documents stream the state of the real unit.
Cleaned, contextualised, tag-mapped — one truth for the twin.
Physics and ML models keep the replica in step with the plant.
What-if simulation, predictions and training, fed back to operators.
“The safest place to experiment with your plant is the twin that behaves exactly like it.”
From the field · SMHcoders
How is the twin kept in sync with the plant?
Live plant data streams in via ComGate / OPC UA, and the twin is periodically reconciled against plant history so drift is caught and corrected.
What data do we need to start?
Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.
How long until we see results?
A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.
Where does it run, and who sees our data?
On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.
Who owns the result?
You do. Handover includes source code, documentation, training sessions and a retraining plan.