TY - CHAP A1 - Grote-Ramm, Wolfgang A1 - Schönig, Felix A1 - Schwarzbözl, Peter A1 - Drexelius, Maximilian A1 - Maldonado Quinto, Daniel A1 - Binder, Matthias T1 - Model Predictive Control and Service Life Monitoring for Molten Salt Solar Power Towers T2 - 29th SolarPACES Conference, Sydney N2 - A two-component system for control and monitoring of solar power towers with molten salt receivers is proposed. The control component consists of a model predictive control applica-tion (MPC) with a flexible objective function and on-line tunable weights, which runs on a In-dustrial PC and uses a reduced order dynamic model of the receiver’s thermal and flow dy-namics. The second component consists of a service-life monitoring unit, which estimates the service-life consumption of the absorber tubes depending on the current mode of operation based on thermal stresses and creep fatigue in the high temperature regime. The calculation of stresses is done based on a detailed finite element study, in which a digital twin of the re-ceiver was developed. By parallelising the model solver, the estimation of service-life con-sumption became capable of real-time operation. The system has been implemented at a test facility in Jülich, Germany, and awaits field experiments. In this paper, the modeling and archi-tecture are presented along simulation results, which were validated on a hardware-in-the-loop test bench. The MPC showed good disturbance rejection while respecting process variable constraints during the simulation studies. KW - Solar Power Tower KW - Molten Salt KW - Model Predictive Control KW - Dynamic Modelling KW - Service-life Monitoring Y1 - 2023 N1 - Eingereichter Beitrag ER - TY - CHAP A1 - Schramm, Alexander A1 - Kruger, Karl A1 - Niemann, Jörg A1 - Grote, Wolfgang ED - Dimitrov, Dimiter ED - Hagedorn-Hansen, Devon ED - Leipzig, Karl von T1 - Possibilities, Limitations and Considerations for Eye Tracking in Industrial Environments: Experience from a Case Study T2 - International Conference on Competitive Manufacturing (COMA 19) proceedings : 30 January 2019-1 February 2019, Stellenbosch, South Africa N2 - Eye tracking is a measurement technique for determining the orientation and viewing direction of an individual’s eyes. It allows for an evaluation of the individual’s gaze and other related information.There is a wide variety of application areas for eye tracking, however the usage in engineeringindustry is still limited. This paper presents a case study for the use of eye tracking in a testingstation for electrical circuit breakers. The case study aims to evaluate the measurement and analysispossible with the Tobii wearable eye tracking system. From the case study, possibilities, limitationsand considerations for eye tracking in industrial settings are identified and discussed. The paperconcludes that while eye tracking can be used to effectively capture and visualize insightfulinformation, it faces major challenges related to subjectivity in analysis and labour intensiveness. KW - Eye Tracking Y1 - 2019 SN - 978-0-7972-1779-9 SP - 390 EP - 396 PB - Department of Industrial Engineering, Stellenbosch University CY - Stellenbosch ER - TY - CHAP A1 - Beckers, Sebastian A1 - Pawlik, Julian A1 - Kiel, Jürgen A1 - Grote, Wolfgang T1 - Simulation von Partikelflugbahnen zur Auslegungshilfe von elektrostatischen Luftfiltern T2 - Tagungsband: ASIM Workshop 2019 - Simulation Technischer Systeme Grundlagen und Methoden in Modellbildung und Simulation, 21.-22. Februar 2019, Braunschweig Y1 - 2019 SN - 978-3-901608-06-3 U6 - https://doi.org/10.11128/arep.57 SP - 147 EP - 152 PB - ARGESIM, ASIM CY - Wien ER - TY - CHAP A1 - Grote-Ramm, Wolfgang A1 - Schönig, Felix T1 - Model Predictive Control of Solar Receivers T2 - 25th Cologne Solar Colloquium (DLR) Y1 - 2022 UR - https://www.dlr.de/sf/PortalData/73/Resources/dokumente/soko/soko2022/DLR_Solar_Colloquium_2022_Model_Predictive_Control_GROTE-RAMM.pdf ER - TY - CHAP A1 - Lorenzen, Finn A1 - Schönig, Felix A1 - Grote-Ramm, Wolfgang A1 - Oliveira Brito, Marcel A1 - Neuberger, Heiko A1 - Sobolyev, Alexander T1 - Continual Adaptation of Compressor Maps During Operation on Measurement Data For Increased Performance of Model-Based Applications in Turbomachinery T2 - ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, June 26–30, 2023 Boston, Massachusetts, USA N2 - Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this “concept drift”, as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery. KW - compressors KW - Intelligent control KW - Neural networks KW - Modeling Y1 - 2023 SN - 978-0-7918-8697-7 U6 - https://doi.org/10.1115/GT2023-103458 VL - Volume 4 SP - 1 EP - 7 PB - ASME CY - Boston ER - TY - CHAP A1 - Stöhr, Marcel A1 - Zielke, Thomas ED - Leipzig, Konrad von ED - Sacks, Natasha ED - Mc Clelland, Michelle T1 - Machine Learning for Soft Sensors and an Application in Cement Production T2 - Smart, Sustainable Manufacturing in an Ever-Changing World Proceedings of International Conference on Competitive Manufacturing (COMA ’22) KW - Soft sensors KW - Machine learning KW - Dynamic processes KW - Cement production KW - Robustness KW - Artificial intelligence Y1 - 2023 SN - 978-3-031-15604-5 U6 - https://doi.org/10.1007/978-3-031-15602-1_46 VL - Lecture Notes in Production Engineering SP - 627 EP - 638 PB - Springer CY - Cham ER - TY - CHAP A1 - Prusas, Benedik T1 - Simulation of Germany’s security of supply: long-term forecasting of electric load profiles using machine learning. T2 - ENERDAY 2022 - 16th international conference of energy economics and technology, 30.9.2022, Dresden KW - KiVi KW - Vortrag Y1 - 2022 PB - Technische Universität Dresden CY - Dresden ER - TY - CHAP A1 - Kampann, Maximilian A1 - Goebel, Johannes A1 - Adam, Mario ED - Wesselak, Viktor T1 - Effizienzuntersuchungen an einem Wärmepumpensystem mit Schichtspeicher für Raumheizung und Trinkwarmwasser anhand von Hardware-in-the-Loop Versuchen T2 - Tagungsband: 6. Regenerative Energietechnik Konferenz in Nordhausen, 9.-10.2.2023 Y1 - 2023 UR - https://www.hs-nordhausen.de/fileadmin/Dateien/Forschung/2021/Tagungsband_RETCon_2023_Web.pdf SN - 978-3-940820-21-1 SP - 176 EP - 188 PB - Nordhausen CY - Hochschule Nordhausen ER - TY - CHAP A1 - Neumann, Hannah A1 - Adam, Mario A1 - Backes, Klaus A1 - Börner, Martin A1 - Clees, Tanja A1 - Doetsch, Christian A1 - Glaeser, Susanne A1 - Herrmann, Ulf A1 - May, Johanna A1 - Rosenthal, Florian A1 - Sauer, Dirk Uwe A1 - Stadler, Ingo T1 - Development of open educational resources for renewable energy and the energy transition process T2 - ISES SWC 2021: Solar World Congress Virtual Conference, 25.-29. Oktober 2021 KW - OER4EE Y1 - 2021 ER - TY - CHAP A1 - Gottschald, Joans T1 - Data Driven Model Predictive Control of a District Heating Network T2 - Smart Energy Systems International Conference 2021, 21.09.2021, KW - BestHeatNet Y1 - 2021 CY - Düsseldorf ER -