TY - CHAP A1 - Crookston, Brian M. A1 - Bung, Daniel Bernhard ED - Ortega-Sánchez, Miguel T1 - Application of RGB-D cameras in hydraulic laboratory studies T2 - Proceedings of the 39th IAHR World Congress N2 - Non-intrusive measuring techniques have attained a lot of interest in relation to both hydraulic modeling and prototype applications. Complimenting acoustic techniques, significant progress has been made for the development of new optical methods. Computer vision techniques can help to extract new information, e. g. high-resolution velocity and depth data, from videos captured with relatively inexpensive, consumer-grade cameras. Depth cameras are sensors providing information on the distance between the camera and observed features. Currently, sensors with different working principles are available. Stereoscopic systems reference physical image features (passive system) from two perspectives; in order to enhance the number of features and improve the results, a sensor may also estimate the disparity from a detected light to its original projection (active stereo system). In the current study, the RGB-D camera Intel RealSense D435, working on such stereo vision principle, is used in different, typical hydraulic modeling applications. All tests have been conducted at the Utah Water Research Laboratory. This paper will demonstrate the performance and limitations of the RGB-D sensor, installed as a single camera and as camera arrays, applied to 1) detect the free surface for highly turbulent, aerated hydraulic jumps, for free-falling jets and for an energy dissipation basin downstream of a labyrinth weir and 2) to monitor local scours upstream and downstream of a Piano Key Weir. It is intended to share the authors’ experiences with respect to camera settings, calibration, lightning conditions and other requirements in order to promote this useful, easily accessible device. Results will be compared to data from classical instrumentation and the literature. It will be shown that even in difficult application, e. g. the detection of a highly turbulent, fluctuating free-surface, the RGB-D sensor may yield similar accuracy as classical, intrusive probes. Y1 - 2022 SN - 978-90-832612-1-8 U6 - https://doi.org/10.3850/IAHR-39WC252171192022964 SN - 2521-7119 (print) SN - 2521-716X (online) N1 - 39th IAHR World Congress, 19. - 24. Juni 2022, Granada SP - 5127 EP - 5133 PB - International Association for Hydro-Environment Engineering and Research (IAHR) CY - Madrid ER - TY - JOUR A1 - Cheenakula, Dheeraja A1 - Hoffstadt, Kevin A1 - Krafft, Simone A1 - Reinecke, Diana A1 - Klose, Holger A1 - Kuperjans, Isabel A1 - Grömping, Markus T1 - Anaerobic digestion of algal–bacterial biomass of an Algal Turf Scrubber system JF - Biomass Conversion and Biorefinery N2 - This study investigated the anaerobic digestion of an algal–bacterial biofilm grown in artificial wastewater in an Algal Turf Scrubber (ATS). The ATS system was located in a greenhouse (50°54′19ʺN, 6°24′55ʺE, Germany) and was exposed to seasonal conditions during the experiment period. The methane (CH4) potential of untreated algal–bacterial biofilm (UAB) and thermally pretreated biofilm (PAB) using different microbial inocula was determined by anaerobic batch fermentation. Methane productivity of UAB differed significantly between microbial inocula of digested wastepaper, a mixture of manure and maize silage, anaerobic sewage sludge, and percolated green waste. UAB using sewage sludge as inoculum showed the highest methane productivity. The share of methane in biogas was dependent on inoculum. Using PAB, a strong positive impact on methane productivity was identified for the digested wastepaper (116.4%) and a mixture of manure and maize silage (107.4%) inocula. By contrast, the methane yield was significantly reduced for the digested anaerobic sewage sludge (50.6%) and percolated green waste (43.5%) inocula. To further evaluate the potential of algal–bacterial biofilm for biogas production in wastewater treatment and biogas plants in a circular bioeconomy, scale-up calculations were conducted. It was found that a 0.116 km2 ATS would be required in an average municipal wastewater treatment plant which can be viewed as problematic in terms of space consumption. However, a substantial amount of energy surplus (4.7–12.5 MWh a−1) can be gained through the addition of algal–bacterial biomass to the anaerobic digester of a municipal wastewater treatment plant. Wastewater treatment and subsequent energy production through algae show dominancy over conventional technologies. KW - Biogas KW - Methane KW - Algal Turf Scrubber KW - Algal–bacterial bioflm KW - Circular bioeconomy Y1 - 2022 U6 - https://doi.org/10.1007/s13399-022-03236-z SN - 2190-6823 N1 - Corresponding author: Dheeraja Cheenakula VL - 13 SP - 15 Seiten PB - Springer CY - Berlin ER - TY - CHAP A1 - Blanke, Tobias A1 - Schmidt, Katharina S. A1 - Göttsche, Joachim A1 - Döring, Bernd A1 - Frisch, Jérôme A1 - van Treeck, Christoph ED - Weidlich, Anke ED - Neumann, Dirk ED - Gust, Gunther ED - Staudt, Philipp ED - Schäfer, Mirko T1 - Time series aggregation for energy system design: review and extension of modelling seasonal storages T2 - Energy Informatics N2 - Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results. KW - Energy system KW - Renewable energy KW - Mixed integer linear programming (MILP) KW - Typical periods KW - Time-series aggregation Y1 - 2022 U6 - https://doi.org/10.1186/s42162-022-00208-5 SN - 2520-8942 N1 - 11th DACH+ Conference on Energy Informatics, 15-16 September 2022, Freiburg, Germany VL - 5 IS - 1, Article number: 17 PB - Springer Nature ER -