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Sustainability sponsorship
(2024)
Der AI Act
(2024)
The significance of digital technologies in the context of digitizing production processes, such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of research is the optimization of digital twins through Reinforcement Learning (RL). This necessitates a simulation environment that can be integrated with RL. One is introduced in this paper as the Digital Model Playground (DMPG). The paper outlines the implementation of the DMPG, followed by demonstrating its application in optimizing production scheduling through RL within a sample process. Although there is potential for further development, the DMPG already enables the modeling and optimization of production processes using RL and is comparable to commercial discrete event simulation software regarding the simulation-speed. Furthermore, it is highly flexible and adaptable, as shown by two projects, which distribute the DMPG to a high-performance cluster or generate 2D/3D-Visualization of the simulation model with Unreal. This establishes the DMPG as a valuable tool for advancing the digital transformation of manufacturing systems, affirming its potential impact on the future of production optimization. Currently, planned extensions include the integration of more optimization algorithms and Process Mining techniques, to further enhance the usability of the framework.
In this research work, the calculation of carbon dioxide emissions for trailer traffic based on sensor data is described. The data consist of trailer type, load, trip and route parameters. While the individual trailer parameters can be derived from the order management, the trip and route parameters are collected by frequently data. With this, the transport carbon footprint is calculated based on DIN EN 16258 by means of the ProBas database. The distributions of carbon dioxide emissions in trailer traffic are analysed and discussed. The results of the case study indicate that sensor-based modelling can be a useful tool for an improved transparency and estimation of carbon dioxide emissions.
Enhancing Digital Twins for Production through Process Mining Techniques : A Literature Review
(2023)
A digital twin (DT) plays a vital role in the advancement of manufacturers towards Industry 4.0. However, the creation and maintenance of DTs can be time-consuming. One approach to streamline this process is the utilization of process mining (PM) methods and techniques, which can automatically generate valuable information for DTs. Therefore, this paper aims to examine different approaches that augment DTs with PM and explore their effects. The review categorizes these approaches into three groups: theoretical approaches, approaches with laboratory case studies, and approaches with real-world case studies conducted by manufacturers. The review reveals that the use of PM can enhance the flexibility and sustainability of DTs. However, this improvement comes at the cost of requiring high-quality data and more data preparation efforts.
Action recognition technology has gained significant traction in recent years. This paper focuses on evaluating neural network architectures for action recognition in the production industry. By utilizing datasets tailored for production or assembly tasks, various architectures are assessed for their accuracy and performance. The findings of this study provide some insights and guidance for researchers and practitioners to select an appropriate architecture or pretrained models for action recognition in the production industry.
The transformation of an energy system is complex and requires not only technical measures, innovation and changes in lifestyle, but also increasing agricultural land use, especially for the construction of wind turbines or solar panels. Land use for biofuel cultivation is also relevant. Participants in transdisciplinary workshops use the simulation tool 100prosim to create and discuss their new energy scenario and learn about the complexity and requirements of the energy transitions regarding land use for renewable energy. The goal is to create an energy system that is supplied completely through renewable energy, while maintaining the balance between consumption and production. The focus is not on economic but on ecological factors and those related to land use. The holistic view of the tool and the visualization make it possible for participants to get an idea of the size and use of the energy production areas planned. Consequently, participants can identify possible conflicts of use, such as feed production and food security, to be discussed as a basic requirement for determining spa ce for opportunities and a desirable optimum.
There are many options for the utilization of biogas in different energy sectors (power, heat, mobility). The technical possibilities of using biogas are more diverse than the actual business models applied in the biogas industry. This paper shows the possible utilization pathways of biogas, divided into coupled power and heat generation, direct utilization and upgrading to a gas of a higher value. Subsequently, an overview of the business models discussed is given by a systematic literature review. The latter shows that the investigation of biogas business models is focused mainly on the last decade and has increased slightly over time. The regions of investigation can be found worldwide, with a clear focus on Europe. Direct use is studied mainly in the Asian and African regions. In the European context, a shift from investigating combined heat and power use to upgrading the biogas produced is evident.