FG Thermische Energietechnik
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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.
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.
To increase the efficiency of PEM electrolysis, simulation models are required that accurately describe the system's electrochemical and thermal behavior in a computationally efficient manner and are thus suitable for developing control strategies. Therefore, a pseudo-2D PEM electrolyzer model is presented in this paper, which is a compromise between the previously developed models regarding their model complexity. The electrochemical behavior is described with equations commonly used in the literature and the thermal behavior with correlations for gas-liquid heat transfer. Preliminary validation indicates that the model can describe the electrochemical behavior and thermal dynamics of a PEM electrolysis stack with good accuracy.