Digital Twin
- Simulates product and process behavior across virtual operating conditions.
- Reinforcement Learning optimizes technical performance and production economics.
Seven assessment dimensions, one explained decision.
Seven assessment dimensions, one explained decision
Product fit
Does it meet texture, nutrition, sensory, quality and product targets?
Equipment compatibility
Can it run on the selected extruder or production line?
Process stability
Is the predicted operating window wide and stable enough?
Scalability
Can it move from laboratory or pilot scale to commercial production?
Economics
Does it meet ingredient, energy, throughput, waste and cost targets?
Confidence
How much relevant, reliable evidence supports the recommendation?
Validation status
Is it generated, simulated, physically tested or production-verified?
Strong product fit, high equipment compatibility, acceptable economics and sufficient simulation confidence. Proceed to one controlled confirmation run.
- Product fit
- Meets targets
- Equipment compatibility
- Within window
- Process stability
- Workable window
- Scalability
- Plausible, to confirm
- Economics
- Within cost target
- Confidence
- Moderate
- Validation status
- Simulated
Missing evidence
Ingredient-variability data across supplier lots, and measured behavior at full throughput.
Recommended next action
Expert approval of the test plan, then one controlled confirmation run with QA sampling.
Conceptual example of the Meallions decision output — not a customer result.
Generated is not verified. Simulated is not production-proven. Meallions makes the evidence level visible at every stage — with a label and an icon, never color alone.
Generated is not verified. Simulated is not production-proven. Meallions makes the evidence level visible at every stage — with a label and an icon, never color alone.
Twin simulation
Digital twin of the extrusion line
- Predicted yield
- 98.2%+1.4
- Energy / t
- 184 kWh
- Throughput
- 3.6 t/h
- Moisture exit
- 11.8%
Operating window · die pressure
Predict the batch before you start it
The twin models your specific line — screw configuration, die geometry, moisture, throughput. It returns expected yield, nutrition retention and energy use, plus the operating window that keeps the batch on spec.
- Compare scenarios side-by-side: cost vs. nutrition vs. throughput.
- Catch off-spec risk hours before raw material is committed.
- Approved scenario becomes the setpoint package for the line.
Real screens from the operator console.
Light-theme panels straight from platform.meallions.com — status bar, equipment tabs, telemetry, and alarms.
Equipment overview
DG75-II twin-screw extruder status, configuration and production context in one view.
A configured twin per line that predicts product and process behavior before a run.
Equipment models, scenario simulation, sensitivity analysis, operating-window prediction and predicted-versus-actual tracking.
Continue through the platform