Digital Process Twin: experimenting without stopping the line
Within Finvalia’s roadmap towards a smart and sustainable factory, the new Digital Process Twin has become the “virtual test room” where engineers and plant managers can ask what-if questions… without taking real-world risks or incurring real-world costs.
What is it and how is it built?
The twin combines three data sources:
- Real-time signals from IIoT sensors (temperature, pressure, flow rate).
- Historical recipes and quality data stored in the corporate data lake.
- Multiphysics simulation models that reproduce heat transfer, moisture, and board flow rate during pressing and drying.
All of this is synchronized in a cloud platform that updates the line’s “virtual state” every few seconds.
What is it for?
| “What-if” scenario | Typical question | Resulting decision |
|---|---|---|
| 💦 Incoming moisture +3% | How much should I extend the press cycle? | Increase temperature by 5°C and extend by 15 s. |
| ♻️ Resin change | Will it affect final density? | Simulate the curing curve before the physical trial. |
| 🚚 Demand up 20% | Where is the bottleneck? | Redesign the layout and add a temporary buffer. |
Benefits already quantified
- -8% waste in pilot batches, by preventing out-of-spec mixes.
- -5% energy consumption thanks to automatic optimization of the thermal curve.
- 48 hours less to validate new formulas, shortening the R&D cycle.
Practical case: melamine at Finsa
During the latest fine-tuning, the twin proposed a roller adjustment sequence that reduced surface marks by 30% before producing the first physical board. Without the model, it would have taken a full shift of trials and scrap material.
Next steps
- Connect the twin to the WMS (Warehouse Management System) and the maintenance system to generate predictive work orders.
- Integrate explainable AI algorithms that suggest actions and show the logic behind each recommendation.
- Extend the simulation to Puertas Vales and Couceiro, harmonizing parameters across plants.
With this tool, Finvalia turns every hypothesis into a safe, fast, low-cost experiment—accelerating continuous improvement and reducing the environmental footprint of engineered wood.
