TY - GEN A1 - Jana, Sayantan A1 - Chauhan, Nikhil A1 - Muthukumar, Palanisamy A1 - Röntzsch, Lars T1 - Transient analysis and performance prediction of a metal hydride based thermal energy storage system with Integrated cooling and heat upgradation T2 - Heat transfer engineering : an international journal N2 - This work embodies a numerical model development and simulation of a metal hydride-based thermal energy storage system with integrated cooling as well heat upgradation. The concept of this system is unique as it operates on thermal drive during the energy storage cycle while during the energy release cycle it is compressor-operated. The utility of the mechanical compressor in the system is two-fold i.e., one operation mode would harness cooling, and another operation mode would fetch heat upgradation. The numerical model devised is a lumped parameter model which solves for hydrogen concentration and temperature of the linked metal hydride reactors. The proposed thermal energy storage system is of 5 MJ capacity which engages tube bundle reactor settings for both high-temperature and low-temperature hydride alloys. Each energy storage and release cycles are simulated for a fixed duration of 10000 s. For a fixed heat supply and heat recovery temperatures of 623 K and 573 K, the system offered cooling coefficient of performance in the range of 1.43–1.58 at 283–293 K. Also, approximately 77% of the energy stored at 623 K could be retrieved at 573 K. In heat upgradation operation mode, 10–30 K temperature lift was accomplished. KW - Metal Hydride KW - Thermal Energy Storage System KW - Simulation Y1 - 2025 U6 - https://doi.org/10.1080/01457632.2025.2521599 SN - 0145-7632 SP - 1 EP - 13 PB - Taylor & Francis CY - London ER - TY - GEN A1 - Sakkas, Nikolaos Panagiotis A1 - Abang, Roger Atini T1 - Thermal load prediction of communal district heating systems by applying data-driven machine learning methods T2 - Energy Reports N2 - 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. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S2352484721015213 U6 - https://doi.org/10.1016/j.egyr.2021.12.082 SN - 2352-4847 VL - 8 SP - 1883 EP - 1895 ER - TY - GEN A1 - Katzer, Christian A1 - Babul, Konrad A1 - Klatt, Matthias A1 - Krautz, Hans Joachim T1 - Quantitative und qualitative relationship between swirl burner operating conditions and pulverized coal flame length T2 - Fuel Processing Technology KW - Flame length KW - Flame stability KW - Swirl burner KW - Pulverized coal KW - Image processing Y1 - 2017 U6 - https://doi.org/10.1016/j.fuproc.2016.10.013 SN - 0378-3820 VL - 156 SP - 138 EP - 155 ER - TY - GEN A1 - Ziems, Christian A1 - Tannert, Daniel A1 - Krautz, Hans Joachim T1 - Project presentation: Design and installation of advanced high pressure alkaline electrolyzer-prototypes T2 - Energy Procedia Y1 - 2012 U6 - https://doi.org/10.1016/j.egypro.2012.09.087 SN - 1876-6102 VL - 29 SP - 744 EP - 753 ER - TY - GEN A1 - Kangwanpongpan, Tanin A1 - Correa da Silva, Rodrigo A1 - Krautz, Hans Joachim T1 - Prediction of oxy-coal combustion through an optimized weighted sum of gray gases model T2 - Energy : the international journal Y1 - 2011 U6 - https://doi.org/10.1016/j.energy.2011.06.010 SN - 0360-5442 VL - 41 IS - 1 SP - 244 EP - 251 ER - TY - GEN A1 - Missagia, Bruna A1 - Guerrero, Cinthya A1 - Narra, Satyanarayana A1 - Sun, Yaoming A1 - Ay, Peter A1 - Krautz, Hans Joachim T1 - Physicomechanical properties of rice husk pellets for energy generation Y1 - 2011 ER - TY - GEN A1 - Danila, Kevin A1 - Vollmer, Jan A1 - Kunz, Philip T1 - Performance evaluation of reanalysis models for upsampling of solar irradiance and wind speed data T2 - Renewable energy N2 - The transition to renewable energy sources is a cornerstone of modern energy system transformation. Yet, the integration of solar and wind power remains challenging due to limited availability of high-resolution time series data. Accurate modeling and forecasting of renewable energy generation are essential for grid stability, and the effective deployment of technologies such as water electrolysis. However, most available datasets are provided at coarse temporal resolutions, which restricts their utility for dynamic system analysis and hinders the development of robust operational strategies. This study addresses this critical gap by evaluating the performance of reanalysis models and introducing a statistical upsampling approach that generates data with 1-min resolution from reanalysis data with 1-h resolution using a first-order Markov chain Monte Carlo method. The method is validated against measured data. The normalized root-mean-square error of the simulated data is less than 10% and the mean intra-hour standard deviation differs from 4.8% to 9.1% compared to measured values for solar irradiance and wind speed. The generated data facilitates the optimization of system operation, and enhances the planning of sector-coupled infrastructures. Ultimately, this approach enables reliable integration of renewable energy despite limited data availability and thus contributes to the advancement of the sustainable energy transition. KW - Renewable energy source KW - Electrification KW - Reanalysis KW - Modeling KW - Monte Carlo KW - Markov Y1 - 2026 U6 - https://doi.org/10.1016/j.renene.2026.125301 SN - 0960-1481 VL - 261 SP - 1 EP - 13 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Asegehegn, Teklay Weldeabzgi A1 - Schreiber, Matthias A1 - Krautz, Hans Joachim T1 - Numerical Study of Bubbling Gas-Solid Fluidized Beds Hydrodynamics: Influence of Immersed Horizontal Tubes and Data Analysis Y1 - 2011 UR - http://www.bepress.com/ijcre/vol9/A16 ER - TY - GEN A1 - Asegehegn, Teklay Weldeabzgi A1 - Schreiber, Matthias A1 - Krautz, Hans Joachim T1 - Numerical simulation and experimental validation of bubble behavior in 2D gas-solid fluidized beds with immersed horizontal tubes Y1 - 2011 U6 - https://doi.org/http://dx.doi.org/10.1016/j.ces.2011.07.056 ER - TY - GEN A1 - Schreiber, Matthias A1 - Asegehegn, Teklay Weldeabzgi A1 - Krautz, Hans Joachim T1 - Numerical and Experimental Investigation of Bubbling Gas-Solid Fluidized Beds with Dense Immersed Tube Bundles Y1 - 2011 ER -