Dataset of an experimental campaign of a Digital Twin for a biomass-to-SNG pilot plant

This dataset contains the results of the Digital Twin for a biomass-to-SNG pilot plant created within the ADORe-SNG project. 

Context and methodology

• A Digital Twin was created for a biomass-to-SNG pilot plant at TU Wien.
• The plant was automated and optimised using model predictive control (MPC), online process simulation and a soft sensor.
• The data presented here is the output of these software tools that controlled the plant and were presented to the operators in real time.
• The plant data stems from an excerpt of 9.5 hours from an experimental campaign in November 2023

The dataset accompanies a publication wherein further details regarding methods can be found.

Technical details

• The data consist of four files:
• Two CSV files with the outputs of the MPC
• Data_MPC_DFB.csv with a sampling rate of 5 seconds
• Data_MPC_Syngas.csv with a sampling rate of 1 second
• One CSV file with the results of the soft sensor
• Data_SoftSensor.csv with a sampling rate of 1 second retimed to 1 minute
• One JSON file with the inputs and outputs of the online process simulation in the software IPSEpro
• Data_IPSE.json with a sampling rate of 1 minute
• The variable names in the files are explained in the attached README.txt

Further details

For further details see the publication “Design and Implementation of a Digital Twin for a Biomass-to-Gas plant” by Stefan Jankovic, Lukas Stanger, Alexander Bartik, Martin Hammerschmid, Florian Benedikt, Michael Mittermayr, Matthias Binder, Martin Kozek and Stefan Müller submitted to “Applied Energy”