Model-Based Control for Power-to-X Platforms: Knowledge Integration for Digital Twins

2025-07-04

AI Summary

Addressing the challenges of adaptive process control under highly fluctuating operational conditions, heterogeneous model interoperability, and insufficient knowledge integration in digital twins for offshore Power-to-X platforms, this paper proposes a graph-structured knowledge representation framework integrating semantic web technologies with model-driven engineering. Standardized behavioral modeling and port-matching mechanisms enable automatic discovery, semantic alignment, dynamic configuration, and seamless switching of multi-source heterogeneous models. A scalable knowledge graph is implemented using Neo4j, with structured model information automatically extracted from Asset Administration Shells. The approach significantly enhances the autonomous decision-making capability and environmental responsiveness of digital twin systems. Experimental validation in a real-world offshore Power-to-X setting demonstrates the method’s efficacy and reusability in knowledge integration, confirming its feasibility for complex, dynamic industrial applications.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

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.

Problem

• Adaptive process control for volatile offshore Power-to-X platforms
• Integration of heterogeneous models in Digital Twins
• Automatic model adaptation and selection using semantic technologies

Innovation

• Standardized behavior models for Digital Twins
• Graph-based knowledge representation with Neo4j
• Automatic data extraction from Asset Administration Shells