Model-Based Control for Power-to-X Platforms: Knowledge Integration for Digital Twins
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912465750589440 |
|---|---|
| author | Dittler, Daniel Frank, Peter Hildebrandt, Gary Peterson, Luisa Jazdi, Nasser Weyrich, Michael |
| author_facet | Dittler, Daniel Frank, Peter Hildebrandt, Gary Peterson, Luisa Jazdi, Nasser Weyrich, Michael |
| contents | Offshore Power-to-X platforms enable flexible conversion of renewable energy, but place high demands on adaptive process control due to volatile operating conditions. To face this challenge, using Digital Twins in Power-to-X platforms is a promising approach. Comprehensive knowledge integration in Digital Twins requires the combination of heterogeneous models and a structured representation of model information. The proposed approach uses a standardized description of behavior models, semantic technologies and a graph-based model understanding to enable automatic adaption and selection of suitable models. It is implemented using a graph-based knowledge representation with Neo4j, automatic data extraction from Asset Administration Shells and port matching to ensure compatible model configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03553 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-Based Control for Power-to-X Platforms: Knowledge Integration for Digital Twins Dittler, Daniel Frank, Peter Hildebrandt, Gary Peterson, Luisa Jazdi, Nasser Weyrich, Michael Computational Engineering, Finance, and Science Offshore Power-to-X platforms enable flexible conversion of renewable energy, but place high demands on adaptive process control due to volatile operating conditions. To face this challenge, using Digital Twins in Power-to-X platforms is a promising approach. Comprehensive knowledge integration in Digital Twins requires the combination of heterogeneous models and a structured representation of model information. The proposed approach uses a standardized description of behavior models, semantic technologies and a graph-based model understanding to enable automatic adaption and selection of suitable models. It is implemented using a graph-based knowledge representation with Neo4j, automatic data extraction from Asset Administration Shells and port matching to ensure compatible model configurations. |
| title | Model-Based Control for Power-to-X Platforms: Knowledge Integration for Digital Twins |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2507.03553 |