@inproceedings{SattlerChicoCaminosUerlingsetal.2020, author = {Sattler, Johannes Christoph and Chico Caminos, Ricardo Alexander and {\"U}rlings, Nicolas and Dutta, Siddharth and Ruiz, Victor and Kalogirou, Soteris and Ktistis, Panayiotis and Agathokleous, Rafaela and Jung, Christian and Alexopoulos, Spiros and Atti, Vikrama Nagababu and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf}, title = {Operational experience and behaviour of a parabolic trough collector system with concrete thermal energy storage for process steam generation in Cyprus}, series = {AIP Conference Proceedings}, booktitle = {AIP Conference Proceedings}, number = {2303}, doi = {10.1063/5.0029278}, pages = {140004-1 -- 140004-10}, year = {2020}, abstract = {As part of the transnational research project EDITOR, a parabolic trough collector system (PTC) with concrete thermal energy storage (C-TES) was installed and commissioned in Limassol, Cyprus. The system is located on the premises of the beverage manufacturer KEAN Soft Drinks Ltd. and its function is to supply process steam for the factory's pasteurisation process [1]. Depending on the factory's seasonally varying capacity for beverage production, the solar system delivers between 5 and 25 \% of the total steam demand. In combination with the C-TES, the solar plant can supply process steam on demand before sunrise or after sunset. Furthermore, the C-TES compensates the PTC during the day in fluctuating weather conditions. The parabolic trough collector as well as the control and oil handling unit is designed and manufactured by Protarget AG, Germany. The C-TES is designed and produced by CADE Soluciones de Ingenier{\´i}a, S.L., Spain. In the focus of this paper is the description of the operational experience with the PTC, C-TES and boiler during the commissioning and operation phase. Additionally, innovative optimisation measures are presented.}, language = {en} } @inproceedings{Laack2020, author = {Laack, Walter van}, title = {Schnittstelle Tod: Aufbruch oder Ende - Kontakte oder Hirngespinste?}, publisher = {van Laack GmbH}, address = {Aachen}, isbn = {978-3-936624-51-9}, pages = {264 Seiten}, year = {2020}, abstract = {Tagungsbeitr{\"a}ge des 6. Europ{\"a}ischen Seminars am 09. November 2019 in Aachen zum Thema Nahtoderfahrungen mit dem Serientitel: "Schnittstelle Tod"}, language = {de} } @inproceedings{EltesterFerreinSchiffer2020, author = {Eltester, Niklas Sebastian and Ferrein, Alexander and Schiffer, Stefan}, title = {A smart factory setup based on the RoboCup logistics league}, series = {2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)}, booktitle = {2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1109/ICPS48405.2020.9274766}, pages = {297 -- 302}, year = {2020}, abstract = {In this paper we present SMART-FACTORY, a setup for a research and teaching facility in industrial robotics that is based on the RoboCup Logistics League. It is driven by the need for developing and applying solutions for digital production. Digitization receives constantly increasing attention in many areas, especially in industry. The common theme is to make things smart by using intelligent computer technology. Especially in the last decade there have been many attempts to improve existing processes in factories, for example, in production logistics, also with deploying cyber-physical systems. An initiative that explores challenges and opportunities for robots in such a setting is the RoboCup Logistics League. Since its foundation in 2012 it is an international effort for research and education in an intra-warehouse logistics scenario. During seven years of competition a lot of knowledge and experience regarding autonomous robots was gained. This knowledge and experience shall provide the basis for further research in challenges of future production. The focus of our SMART-FACTORY is to create a stimulating environment for research on logistics robotics, for teaching activities in computer science and electrical engineering programmes as well as for industrial users to study and explore the feasibility of future technologies. Building on a very successful history in the RoboCup Logistics League we aim to provide stakeholders with a dedicated facility oriented at their individual needs.}, language = {en} } @inproceedings{PaulsenHoffstadtKrafftetal.2020, author = {Paulsen, Svea and Hoffstadt, Kevin and Krafft, Simone and Leite, A. and Zang, J. and Fonseca-Zang, W. and Kuperjans, Isabel}, title = {Continuous biogas production from sugarcane as sole substrate}, series = {Energy Reports}, volume = {6}, booktitle = {Energy Reports}, number = {Supplement 1}, publisher = {Elsevier}, doi = {10.1016/j.egyr.2019.08.035}, pages = {153 -- 158}, year = {2020}, abstract = {A German-Brazilian research project investigates sugarcane as an energy plant in anaerobic digestion for biogas production. The aim of the project is a continuous, efficient, and stable biogas process with sugarcane as the substrate. Tests are carried out in a fermenter with a volume of 10 l. In order to optimize the space-time load to achieve a stable process, a continuous process in laboratory scale has been devised. The daily feed in quantity and the harvest time of the substrate sugarcane has been varied. Analyses of the digester content were conducted twice per week to monitor the process: The ratio of inorganic carbon content to volatile organic acid content (VFA/TAC), the concentration of short-chain fatty acids, the organic dry matter, the pH value, and the total nitrogen, phosphate, and ammonium concentrations were monitored. In addition, the gas quality (the percentages of CO₂, CH₄, and H₂) and the quantity of the produced gas were analyzed. The investigations have exhibited feasible and economical production of biogas in a continuous process with energy cane as substrate. With a daily feeding rate of 1.68gᵥₛ/l*d the average specific gas formation rate was 0.5 m3/kgᵥₛ. The long-term study demonstrates a surprisingly fast metabolism of short-chain fatty acids. This indicates a stable and less susceptible process compared to other substrates.}, language = {en} } @inproceedings{SildatkeKarwanniKraftetal.2020, author = {Sildatke, Michael and Karwanni, Hendrik and Kraft, Bodo and Schmidts, Oliver and Z{\"u}ndorf, Albert}, title = {Automated Software Quality Monitoring in Research Collaboration Projects}, series = {ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops}, booktitle = {ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1145/3387940.3391478}, pages = {603 -- 610}, year = {2020}, abstract = {In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem. Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ. Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible. In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production. Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.}, language = {en} }