Data Driven Simulation

Data-Driven Simulations

AMESA enables you to build simulations from historical data. These simulations are used to train and evaluate multi-agent systems in high-fidelity environments that reflect your actual operations.

Use cases include modeling equipment behavior, logistics workflows, and process automation.

Recommended Data Range

• Minimum: 3 months

• Preferred: 1 year (especially for systems with seasonal variation)

Datasets will likely yield a successful simulation when they reflect a >80% accurate relationship between control actions (operator adjustments) and sensor readings.

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1. CSV Data Format

Upload a CSV file where each column follows a naming convention:

Variable Type	Prefix	Example
Sensor	s_	s_T (temperature)
Action	a_	a_dTc (cooling delta)
Other Input	(no prefix)	Datetime

Sensors are variables that provide information about the environment or conditions within the process. These may be reported by the machine being controlled or they may come from outside systems. Quality measures such as the results of lab tests are also sensors.

Actions are variables that describe the adjustments the operator makes to the system controls.

Optionally include a timestamp column in UTC. If omitted, AMESA assumes the rows are sequential time steps.

Not all fields need values for every row. The simulator recognizes when data is collected at different intervals.

Units Row (Optional)

Include a row above your column headers to define units for each variable:

kmol/m3,K,K,,kmol/m3,K
s_Ca,s_T,s_Tc,s_Tref,s_Cref,a_dTc
…data rows…

2. Data Configuration

After uploading your CSV file, AMESA guides you through a configuration screen where you confirm the role and type of each column:

Field	Description
User variable name	Rename your variables for clarity (optional)
Mode	Sensor / Action / None (for reference values)
Type	Box or continuous variables
Value Range	Auto-calculated from your data
Unit (optional)	Populated from the units row

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3. Simulation Creation Flow

🛠 Step 1: Upload CSV

• Navigate to the Data-Driven Simulator section.

• Name your simulation.

• Upload your CSV file in the proper format.

🧭 Step 2: Configure Variables

• Review detected variables.

• Adjust mode, type, unit, and value range if needed.

📊 Step 3: Review Data Score

AMESA will scan your data and generate a Data Score from 0 to 100:

Score Range	Meaning
80–100	Good quality – Ready to simulate
Below 80	Needs improvement – Add data or clean inputs.

🚀 Step 4: Create Simulation

Click Next, and your simulation will be created. It will now be available in your workspace for training, testing, and analysis.

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4. Best Practices

• Use consistent naming: Prefix sensors and actions properly.

• Include a units row: This improves interpretability and ensures correctness.

• Handle missing data: Some missing values are fine, but complete coverage of key variables improves accuracy.

• Timestamps (optional): Include UTC timestamps if events need temporal alignment.

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5. Example File

📁 Download sample data:

17MB

cstr_simulator_data 2.csv

Open

ple.csv

Use this example file, which contains data, to create a Chemical Process Simulation and see the expected structure, naming conventions, and units in action.

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6. Troubleshooting

Problem	Cause	Fix
Variable not detected	Missing prefix	Use s_ for sensors and a_ for actions
Low data score	Not enough data, missing values	Add more historical data or fill in key columns
Unit not recognized	Unit row missing or misaligned	Add units row directly above the variable row