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”
