@techreport{KrautzMoehlenkampSakkasetal., author = {Krautz, Hans Joachim and M{\"o}hlenkamp, Georg and Sakkas, Nikolaos Panagiotis and Gillung, Frank and Lenivova, Veronika}, title = {Verbundvorhaben AEL3D - Neuartige por{\"o}se 3D-Elektrodenmaterialen zur effizienteren alkalischen Wasserelektrolyse; Teilprojekt: Zellbau und Systemtests : fachlicher Abschlussbericht : Projektlaufzeit: 01.09.2016-31.12.2021}, publisher = {KWT Lehrstuhl Kraftwerkstechnik, BTU Brandenburgische Technische Universit{\"a}t Cottbus-Senftenberg}, address = {Cottbus}, doi = {10.2314/KXP:1859085385}, pages = {147}, language = {de} } @misc{SakkasRoger, author = {Sakkas, Nikolaos Panagiotis and Roger, Abang}, title = {Thermal load prediction of communal district heating systems by applying data-driven machine learning methods}, series = {Energy Reports}, volume = {8}, journal = {Energy Reports}, issn = {2352-4847}, doi = {10.1016/j.egyr.2021.12.082}, pages = {1883 -- 1895}, abstract = {Load forecasting is an essential part of the operational management of combined heat and electrical power units, since a reliable hour- and day-ahead estimation of their thermal and electrical load can significantly improve their technical and economic performance, as well as their reliability. Among different types of prediction techniques, data-driven machine learning methods appear to be more suitable for load estimation in operational systems, compared to the classical forward approach. Research so far has been concentrated mainly on the magnitude of buildings with single load types. It has only been extended to a limited degree on the level of a district heating network where several end users with different characteristics merge into one bigger scale heat consumer (city or group of communities). In this study, artificial neural networks are utilized, to develop a load prediction model for district heating networks. A segmented analytical multi-phase approach is employed, to gradually optimize the predictor by varying the characteristics of the input variables and the structure of the neural network. The comparison against the load prediction time series generated by a local communal energy supplier using a commercial software reveals that, although the latter is enhanced by manual human corrections, the optimized fully automatic predictors developed in the present study generate a more reliable load forecast.}, language = {en} } @techreport{KrautzMoehlenkampKatzeretal., author = {Krautz, Hans Joachim and M{\"o}hlenkamp, Georg and Katzer, Christian and Klatt, Matthias}, title = {Konzepte und Betriebsstrategien f{\"u}r lastflexible Feuerungs- und Dampfsysteme : Abschlussbericht : Laufzeit des Vorhabens: 01.09.2016 bis 31.12.2021}, publisher = {btu Brandenburgische Technische Universit{\"a}t Cottbus-Senftenberg}, address = {Cottbus}, doi = {10.2314/KXP:1861122357}, pages = {223}, language = {de} } @misc{AkayBashkatovCoyetal., author = {Akay, {\"O}mer and Bashkatov, Aleksandr and Coy, Emerson and Eckert, Kerstin and Einarsrud, Kristian Etienne and Friedrich, Andreas and Kimmel, Benjamin and Loos, Stefan and Mutschke, Gerd and R{\"o}ntzsch, Lars and Symes, Mark D. and Yang, Xuegeng and Brinkert, Katharina}, title = {Electrolysis in reduced gravitational environments: current research perspectives and future applications}, series = {npj Microgravity}, volume = {8}, journal = {npj Microgravity}, issn = {2373-8065}, doi = {10.1038/s41526-022-00239-y}, abstract = {Electrochemical energy conversion technologies play a crucial role in space missions, for example, in the Environmental Control and Life Support System (ECLSS) on the International Space Station (ISS). They are also vitally important for future long-term space travel for oxygen, fuel and chemical production, where a re-supply of resources from Earth is not possible. Here, we provide an overview of currently existing electrolytic energy conversion technologies for space applications such as proton exchange membrane (PEM) and alkaline electrolyzer systems. We discuss the governing interfacial processes in these devices influenced by reduced gravitation and provide an outlook on future applications of electrolysis systems in, e.g., in-situ resource utilization (ISRU) technologies. A perspective of computational modelling to predict the impact of the reduced gravitational environment on governing electrochemical processes is also discussed and experimental suggestions to better understand efficiency-impacting processes such as gas bubble formation and detachment in reduced gravitational environments are outlined.}, language = {en} } @misc{ThummarAbangMenzeletal., author = {Thummar, Krunalkumar and Abang, Roger and Menzel, Katharina and Groot, Matheus Theodorus de}, title = {Coupling a Chlor-Alkali Membrane Electrolyzer Cell to a Wind Energy Source: Dynamic Modeling and Simulations}, series = {Energies}, volume = {15}, journal = {Energies}, number = {2}, issn = {1996-1073}, doi = {10.3390/en15020606}, pages = {1 -- 26}, abstract = {Renewable energy sources are becoming a greater component of the electrical mix, while being significantly more volatile than conventional energy sources. As a result, net stability and availability pose significant challenges. Energy-intensive processes, such as chlor-alkali electrolysis, can potentially adjust their consumption to the available power, which is known as demand side management or demand response. In this study, a dynamic model of a chlor-alkali membrane cell is developed to assess the flexible potential of the membrane cell. Several improvements to previously published models were made, making the model more representative of state-of-the-art CA plants. By coupling the model with a wind power profile, the current and potential level over the course of a day was simulated. The simulation results show that the required ramp rates are within the regular operating possibilities of the plant for most of the time and that the electrolyte concentrations in the cell can be kept at the right level by varying inlet flows and concentrations. This means that a CA plant can indeed be flexibly operated in the future energy system.}, language = {en} }