Scenario-based Simulation for Energy Optimization in Learning Factory Environments

Atacan Ketenci*, Matthias Josef Eder, Markus Ritter, Christian Ramsauer

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Caused by the constantly rising energy prices and the demand for green products, the manufacturing industry has to increasingly deal with the topic of energy optimization. Thus, the focus is shifting to the improvement of production facilities in order to minimize resource consumption. When planning a more energy efficient production, it is advisable to set up a continuous monitoring system on the existing equipment to get an insight into the prevailing energy consumption. Based on this, optimization potentials can be identified. Different possibilities for increasing energy efficiency already exist, including e.g. the use of more efficient equipment or the optimal use of the facility. However, realistic assessments of saving potentials are a big challenge. In this paper, a virtual model of a learning factory is created to assess a realistic energy consumption profile. Using currently measured energy data and possible investment activities, scenarios for energy optimization in the assembly line are generated. By evaluating the scenarios using the virtual model, realistic saving potentials can be determined and evaluated, enabling investment planning to be strategically improved through the consideration of energy efficiency
Originalspracheenglisch
TitelProceedings of the Conference on Learning Factories (CLF) 2021
KapitelSustainability & Circular Economy in Learning Factories
Seiten1-6
Seitenumfang6
DOIs
PublikationsstatusVeröffentlicht - Jun 2021
Veranstaltung11th Conference on Learning Factories - Virtuell, Österreich
Dauer: 1 Jul 20212 Jul 2021

Konferenz

Konferenz11th Conference on Learning Factories
KurztitelCLF2021
LandÖsterreich
OrtVirtuell
Zeitraum1/07/212/07/21

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