Industrial Process Technology AI
IndustrialProcessAI™
Digital Twin Intelligence for All Industrial Sectors

Process Data Submission

Submit, structure and map your industrial process data to build an AI-powered Digital Twin.

Sector templateFully customisable — steps, equipment, variables, units and KPIs
Digital Twin Readiness50%

Complete the missing process information to improve model accuracy.

Process steps
Raw MaterialMixingHeatingFryingCoolingPackagingFinal Product
Inputs
Raw material lotIncoming moistureOil temperatureLine speedAmbient humidity
Outputs
Final moistureWater activityColour L*a*b*TextureSensory score
KPIs
Yield %Throughput kg/hEnergy kWh/tOil uptake %Waste %

Preconfigured for Food & Beverage: 5 process variables and 16 run-data columns are loaded. Add, rename, re-unit or delete anything — custom steps, equipment, variables and KPIs are always allowed.

1 — Data Submission Method

Manual Data Entry

Create and enter process data directly.

Excel / CSV Upload

Import historical production, experimental or laboratory data.

Drag & drop your file here — supports .xlsx, .xls, .csv

Live Data Connection

Connect live or historical industrial data sources.

PLCSCADAMESLIMSERPHistorianIoT SensorsAPI

2 — Process Data Structure

A

Run Identification

  • Run ID
  • Batch ID
  • Date
  • Time
  • Production Line
  • Product
  • SKU
  • Operator
B

Raw Material Inputs

  • Material name
  • Material code
  • Supplier
  • Lot number
  • Quantity
  • Unit
  • Moisture
  • Composition / specification
  • Storage condition
C

Process Variables

  • Process step
  • Equipment
  • Variable
  • Setpoint
  • Actual value
  • Unit
  • Minimum
  • Target
  • Maximum
TemperatureTimePressureFlow rateRPMFeed rateLine speedpHMoistureDosage
D

Equipment & Environment

  • Equipment ID
  • Equipment load
  • Ambient temperature
  • Relative humidity
  • Motor current
  • Energy consumption
  • Pressure
  • Equipment status
E

Quality Outputs

  • Quality parameter
  • Result
  • Unit
  • Target
  • Lower specification limit
  • Upper specification limit
  • Test method
MoistureWater activitypHBrixViscosityColor L*a*b*TextureFatSaltProteinParticle sizeGC / HPLC resultsSensory score
F

Performance & Economics

  • Throughput
  • Yield
  • Waste
  • Downtime
  • Energy consumption
  • Material cost
  • Production cost
  • Cost per kg / ton
  • PASS / WARNING / FAIL

AI Data Mapping

Uploaded headers interpreted automatically — every mapping stays editable.

Upload an Excel/CSV dataset to generate AI column mappings.

Digital Twin Data Quality

Overall 79%
Data Completeness92%
Variable Coverage83%
Historical Depth78%
Sensor Coverage48%
Output / KPI Coverage94%
Missing data points0
Duplicate run records0
Unit inconsistencies3
Sensor gaps in the flow2
Mappings needing review0
Highly correlated variables1

Fryer temperature data are sufficient for Digital Twin training, but only 3 production runs are available in the upper operating region. Additional observations are recommended to improve prediction reliability there.

Input → Digital Twin → Output

How your submitted data flows through the Digital Twin engine.

Inputs
  • Raw Materials
  • Formulation
  • Environment
  • Process Settings
  • Equipment Conditions
Industrial Process AI™
Digital Twin Engine
PhysicsML ModelsOptimizerSimulation
Outputs
  • Product Quality
  • Yield
  • Energy
  • Waste
  • Cost
  • Process Stability
  • Predicted Defects
Digital Twin Readiness
50%
0 runs · 16 variables

Readiness Checklist

  • Process flow defined
  • Raw material variables mapped
  • Process parameters mapped
  • Quality outputs identified
  • Historical production data uploaded
  • Environmental data complete
  • Energy data available
  • All sensor tags mapped
Your dataset is suitable for initial Digital Twin development. Adding energy consumption and environmental data could improve optimization capability.