01
Control overview
Equipment state, batch, phase and timing for each dryer at a glance.
Elric WorksProcess equipment · Fluid bed dryingThe dryer, live in 3D, with an AI copilot on the curve.
A digital twin of a fluid bed dryer for Syntegon, with live sensor readings on the model, drying curves, and an AI copilot that flags drift before it becomes a deviation.
- Engagement
- Forward deployed delivery
- Platform
- Desktop application with 3D and AR views
- Used by
- Process engineers and operators
Delivered by Suryanand Sunil, our founder, before he started Valnora & Elric. These projects became part of the company’s portfolio when he founded it.
The challengeWhy this
Why this
was hard.
A fluid bed dryer depends on airflow, temperature and moisture staying in balance. The signals exist, but they usually live on trend screens that are hard to connect to what is happening inside the equipment.
When a drying curve starts to drift, the useful moment to act is early, while the batch can still be saved.
Sensors on the modelEvery reading where it happens.
Every reading where it happens.
On the equipment, not in a list.
The twin places each live reading on the part of the dryer it comes from, and layers in temperature maps and airflow so engineers see the process, not just numbers.
- Inlet airTemperature of the air entering the bed
- Outlet airTemperature leaving the dryer, the key drying signal
- Product temperatureMeasured in the bed itself
- Differential pressureAcross the filter, a sign of clogging
- Air flowVolume through the bed, with a distribution view
- Atomizing airSpray pressure during granulation
- Product moistureTracked against the expected curve
Examples of what the copilot flags
Outlet air temperature is deviating from the expected drying curve.
Filter differential pressure is rising faster than in previous batches.
Moisture reduction rate is within the desired range.
What we builtThe product,
The product,
screen by screen.
02
The live process
Process conditions as they happen, with the drying curve and the readings behind it.
03
Alarms and events
Every alarm with its context, so the team can see what led up to it.
04
Recipes, step by step
Recipe steps and their set points, next to what the equipment is actually doing.
05
Work orders
Maintenance work orders linked to the equipment and the events that triggered them.
06
Reports
Historical reports for batches, alarms and equipment performance.
What changed
Engineers see the dryer, its readings and the drying curve in one place, and the copilot points out drift while there is still time to act.
Capabilities delivered
Real-time 3D digital twinSensor overlays on the modelTemperature and airflow mapsAI process copilotRecipes and work ordersAudit trailAR view
How we worked · Forward deployed deliveryThe Elric method,
The Elric method,
on this engagement.
01 · Frame
The outcome and what "done" means, agreed in writing with the people who run the process.
02 · Ship
Released to real users early, in the plant, the field or the operations team.
03 · Measure
Live data and records in place from the first release, not added at the end.
04 · Hand over
Docs, runbooks and a working session, so the client team owns it.
More work
