Ontology-Based Process Modelling-with Examples of Physical Topologies

Author:

Preisig Heinz A

Abstract

Reductionism and splitting application domain into disciplines and identify the smallest required model-granules, termed ”basic entity” combined with systematic construction of the behaviour equations for the basic entities, yields a systematic approach to process modelling. We do not aim toward a single modelling domain, but we enable to address specific application domains and object inheritance. We start with reductionism and demonstrate how the basic entities are depending on the targeted application domain. We use directed graphs to capture process models, and we introduce a new concept, which we call ”tokens” that enables us to extend the context beyond physical systems. The network representation is hierarchical so as to capture complex systems. The interacting basic entities are defined in the leave nodes of the hierarchy, making the overall model the interacting networks in the leave nodes. Multi-disciplinary and multi-scale models result in a network of networks. We identify two distinct network communication ports, namely ports that exchange tokens and ports that transfer information of tokens in accumulators. An ontology captures the structural elements and the applicable rules and defines the syntax to establish the behaviour equations. Linking the behaviours to the basic entities defines the alphabet of a graphical language. We use this graphical language to represent processes, which has proven to be efficient and valuable. A set of three examples demonstrates the power of the graphical language. The Process Modelling framework (ProMo) implements the ontology-centred approach to process modelling and uses the graphical language to construct process models.

Funder

Norges Forskningsråd

Seventh Framework Programme

Horizon 2020 Framework Programme

Publisher

MDPI AG

Subject

Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Neuro – symbolic AI for materials modelling and processes design;MATEC Web of Conferences;2024

2. Ontology-driven automation of process modelling and simulation;Computer Aided Chemical Engineering;2024

3. Reasoning about Physical Processes in Buildings through Component Stereotypes;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15

4. European standardization efforts from FAIR toward explainable-AI-ready data documentation in materials modelling;2023 3rd International Conference on Applied Artificial Intelligence (ICAPAI);2023-05-02

5. Hybrid and cognitive digital twins for the process industry;Open Engineering;2023-01-01

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