Abstract
Strict requirements on the quality of industrial plant operation together with environmental limits and the pursuit of decreasing energy consumption bring more complexity in automation systems. Simulations and models of industrial processes can be utilized in all the phases of an automation system’s life cycle and they can be used for process design as well as for optimal plant operation. Present methods of design and integration of simulations tasks are inefficient and error-prone because almost all pieces of information and knowledge are handled manually. In this chapter, we describe a simulation framework where all configurations, simulation tasks, and scenarios are obtained from a common knowledge base. The knowledge base is implemented utilizing an ontology for defining a data model to represent real-world concepts, different engineering knowledge as well as descriptions and relations to other domains. Ontologies allow the capturing of structural changes in simulations and evolving simulation scenarios more easily than using standard relational databases. Natively ontologies are used to represent the knowledge shared between different projects and systems. The simulation framework provides tools for efficient integration of data and simulations by exploiting the advantages of formalized knowledge. Two processes utilizing Semantic Web technologies within the simulation framework are presented at the end of this chapter.
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This work was supported by the Christian Doppler Forschungsgesellschaft, the Federal Ministry of Economy, Family and Youth, and the National Foundation for Research, Technology and Development in Austria
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Šindelář, R., Novák, P. (2016). Ontology-Based Simulation Design and Integration. In: Biffl, S., Sabou, M. (eds) Semantic Web Technologies for Intelligent Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-41490-4_10
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DOI: https://doi.org/10.1007/978-3-319-41490-4_10
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