TY - CHAP A1 - Paulsen, Svea A1 - Hoffstadt, Kevin A1 - Krafft, Simone A1 - Leite, A. A1 - Zang, J. A1 - Fonseca-Zang, W. A1 - Kuperjans, Isabel T1 - Continuous biogas production from sugarcane as sole substrate T2 - Energy Reports N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1016/j.egyr.2019.08.035 N1 - 6th International Conference on Energy and Environment Research, ICEER 2019, 22–25 July, University of Aveiro, Portugal VL - 6 IS - Supplement 1 SP - 153 EP - 158 PB - Elsevier ER - TY - JOUR A1 - Hoffstadt, Kevin A1 - Pohen, Gino D. A1 - Dicke, Max D. A1 - Paulsen, Svea A1 - Krafft, Simone A1 - Zang, Joachim W. A1 - Fonseca-Zang, Warde A. da A1 - Leite, Athaydes A1 - Kuperjans, Isabel T1 - Challenges and prospects of biogas from energy cane as supplement to bioethanol production JF - Agronomy N2 - Innovative breeds of sugar cane yield up to 2.5 times as much organic matter as conventional breeds, resulting in a great potential for biogas production. The use of biogas production as a complementary solution to conventional and second-generation ethanol production in Brazil may increase the energy produced per hectare in the sugarcane sector. Herein, it was demonstrated that through ensiling, energy cane can be conserved for six months; the stored cane can then be fed into a continuous biogas process. This approach is necessary to achieve year-round biogas production at an industrial scale. Batch tests revealed specific biogas potentials between 400 and 600 LN/kgVS for both the ensiled and non-ensiled energy cane, and the specific biogas potential of a continuous biogas process fed with ensiled energy cane was in the same range. Peak biogas losses through ensiling of up to 27% after six months were observed. Finally, compared with second-generation ethanol production using energy cane, the results indicated that biogas production from energy cane may lead to higher energy yields per hectare, with an average energy yield of up to 162 MWh/ha. Finally, the Farm²CBG concept is introduced, showing an approach for decentralized biogas production. Y1 - 2020 U6 - https://doi.org/10.3390/agronomy10060821 SN - 2073-4395 VL - 10 IS - 6 PB - MDPI CY - Basel ER - TY - CHAP A1 - Tomic, Igor A1 - Penna, Andrea A1 - DeJong, Matthew A1 - Butenweg, Christoph A1 - Senaldi, Ilaria A1 - Guerrini, Gabriele A1 - Malomo, Daniele A1 - Beyer, Katrin T1 - Blind predictions of shake table testing of aggregate masonry buildings T2 - Proceedings of the 17th World Conference on Earthquake Engineering N2 - In many historical centers in Europe, stone masonry is part of building aggregates, which developed when the layout of the city or village was densified. The analysis of such building aggregates is very challenging and modelling guidelines missing. Advances in the development of analysis methods have been impeded by the lack of experimental data on the seismic response of such aggregates. The SERA project AIMS (Seismic Testing of Adjacent Interacting Masonry Structures) provides such experimental data by testing an aggregate of two buildings under two horizontal components of dynamic excitation. With the aim to advance the modelling of unreinforced masonry aggregates, a blind prediction competition is organized before the experimental campaign. Each group has been provided a complete set of construction drawings, material properties, testing sequence and the list of measurements to be reported. The applied modelling approaches span from equivalent frame models to Finite Element models using shell elements and discrete element models with solid elements. This paper compares the first entries, regarding the modelling approaches, results in terms of base shear, roof displacements, interface openings, and the failure modes. KW - Historical centres KW - Stone masonry KW - Adjacent buildings KW - Shake table test KW - Blind prediction competition Y1 - 2020 N1 - 17th World Conference on Earthquake Engineering, Sendai, Japan, September 27 to October 2, 2021 N1 - (Die Konferenz war ursprünglich für den 13-18 September 2020 angesetzt) ER - TY - CHAP A1 - Sildatke, Michael A1 - Karwanni, Hendrik A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - Automated Software Quality Monitoring in Research Collaboration Projects T2 - ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1145/3387940.3391478 N1 - ICSE '20: 42nd International Conference on Software Engineering, Seoul, Republic of Korea, 27 June 2020 - 19 July 2020 SP - 603 EP - 610 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Philipp, Brauner A1 - Brillowski, Florian Sascha A1 - Dammers, Hannah A1 - Königs, Peter A1 - Kordtomeikel, Frauke Carole A1 - Petruck, Henning A1 - Schaar, Anne Kathrin A1 - Schmitz, Seth A1 - Steuer-Dankert, Linda A1 - Mertens, Alexander A1 - Gries, Thomas A1 - Leicht-Scholten, Carmen A1 - Nagel, Saskia K. A1 - Nitsch, Verena A1 - Schuh, Günther A1 - Ziefle, Martina ED - Mrugalska, Beata ED - Trzcielinski, Stefan ED - Karwowski, Waldemar ED - Nicolantonio, Massimo Di ED - Roossi, Emilio T1 - A research framework for human aspects in the internet of production: an intra-company perspective T2 - Proceedings of the AHFE 2020 N2 - Digitalization in the production sector aims at transferring concepts and methods from the Internet of Things (IoT) to the industry and is, as a result, currently reshaping the production area. Besides technological progress, changes in work processes and organization are relevant for a successful implementation of the “Internet of Production” (IoP). Focusing on the labor organization and organizational procedures emphasizes to consider intra-company factors such as (user) acceptance, ethical issues, and ergonomics in the context of IoP approaches. In the scope of this paper, a research approach is presented that considers these aspects from an intra-company perspective by conducting studies on the shop floor, control level and management level of companies in the production area. Focused on four central dimensions—governance, organization, capabilities, and interfaces—this contribution presents a research framework that is focused on a systematic integration and consideration of human aspects in the realization of the IoP. KW - Human factors KW - Digitalization KW - Acceptance KW - Ethics KW - Human-robot collaboration Y1 - 2020 SN - 978-3-030-51980-3 U6 - https://doi.org/10.1007/978-3-030-51981-0_1 N1 - AHFE 2020 Virtual Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, July 16–20, 2020, USA SP - 3 EP - 17 PB - Springer CY - Cham ER -