Abstract
With modular automation, modular industrial plants use a functional engineering approach, and modules enable plug & produce plant engineering. However, plant configuration is still a largely manual process and often not optimized with respect to the available information. In this contribution, we propose a system and algorithm to support the automation engineer in the process of joining together modules into process pipelines and in their optimization. Our solution is built upon an abstract semantic data model that facilitates the automated matching of pre- and post-condition of modules and of the things that are processed by these modules. The pipeline generation engine is further extended by means of an optimization and ranking algorithm, and thus enables automated inter-module pipeline generation and plant optimization. We evaluate our system by means of a simple fictional use case scenario and prove feasibility, applicability as well as the huge potential for time and cost savings.
Zusammenfassung
Die modulare Automatisierung ermöglicht durch Verwendung eines funktionalen Engineering Ansatzes Plug & Produce in industriellen Anlagen. Das Engineering modularer Anlagen ist dennoch immer noch ein weitgehend manueller Prozess, welcher nicht alle zur Verfügung stehenden Informationen verwendet. In diesem Beitrag wird ein Algorithmus vorgeschlagen, welcher den Automatisierungstechniker beim Verbinden von Modulen zu Prozess-Pipelines unterstützt und welcher ein Optimieren der Anlagentopologie ermöglicht. Ein abstraktes semantisches Datenmodell ist hierbei die Basis für das automatische Vergleichen der Eingangs- und Ausgangsbedingungen der Stoffströme von Modulen und darauf aufbauend für die automatische Planung der Verrohrung der Module. Des Weiteren kann eine Optimierung und/oder Bewertung des Stoffflusses innerhalb der modularen Anlage stattfinden und alternative Anlagenkonfigurationen können vorgeschlagen werden. Zur Evaluierung des Algorithmus wird ein einfaches Anwendungsszenario verwendet, welches die Machbarkeit, Anwendbarkeit und letztlich das immense Potential zur Kostenreduktion demonstriert.
About the authors
Nicolai Schoch is Scientist at ABB Corporate Research in Ladenburg, Germany. His research interests focus on semantics, modeling, analytics and automation for Industry 4.0 and Automation Engineering.
Mario Hoernicke is Senior Principal Scientist at ABB Corporate Research in Ladenburg, Germany. His research interest focusses on the development of new and innovative concepts for automation engineering.
Katharina Stark (née Gohr) is Senior Scientist at ABB Corporate Research in Ladenburg, Germany. Her research interests are workflows and tools in the area of automation engineering.
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