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

TL;DR

This paper presents a model-based control approach for Power-to-X platforms utilizing Digital Twins, integrating heterogeneous models through semantic and graph-based techniques for adaptive process control under volatile conditions.

Contribution

It introduces a standardized, knowledge-driven framework combining semantic technologies and graph databases to enable automatic model adaptation and selection in Digital Twins for Power-to-X platforms.

Findings

Implemented using Neo4j for graph-based knowledge representation

Automatic data extraction from Asset Administration Shells

Enhanced model adaptability for volatile operating conditions

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.