@misc{JanaChauhanMuthukumaretal., author = {Jana, Sayantan and Chauhan, Nikhil and Muthukumar, Palanisamy and R{\"o}ntzsch, Lars}, title = {Transient analysis and performance prediction of a metal hydride based thermal energy storage system with Integrated cooling and heat upgradation}, series = {Heat transfer engineering : an international journal}, journal = {Heat transfer engineering : an international journal}, publisher = {Taylor \& Francis}, address = {London}, issn = {0145-7632}, doi = {10.1080/01457632.2025.2521599}, pages = {1 -- 13}, abstract = {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.}, language = {en} } @misc{SakkasAbang, author = {Sakkas, Nikolaos Panagiotis and Abang, Roger Atini}, 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} } @misc{KatzerBabulKlattetal., author = {Katzer, Christian and Babul, Konrad and Klatt, Matthias and Krautz, Hans Joachim}, title = {Quantitative und qualitative relationship between swirl burner operating conditions and pulverized coal flame length}, series = {Fuel Processing Technology}, volume = {156}, journal = {Fuel Processing Technology}, issn = {0378-3820}, doi = {10.1016/j.fuproc.2016.10.013}, pages = {138 -- 155}, language = {en} } @misc{ZiemsTannertKrautz, author = {Ziems, Christian and Tannert, Daniel and Krautz, Hans Joachim}, title = {Project presentation: Design and installation of advanced high pressure alkaline electrolyzer-prototypes}, series = {Energy Procedia}, volume = {29}, journal = {Energy Procedia}, issn = {1876-6102}, doi = {10.1016/j.egypro.2012.09.087}, pages = {744 -- 753}, language = {en} } @misc{KangwanpongpanCorreadaSilvaKrautz, author = {Kangwanpongpan, Tanin and Correa da Silva, Rodrigo and Krautz, Hans Joachim}, title = {Prediction of oxy-coal combustion through an optimized weighted sum of gray gases model}, series = {Energy : the international journal}, volume = {41}, journal = {Energy : the international journal}, number = {1}, issn = {0360-5442}, doi = {10.1016/j.energy.2011.06.010}, pages = {244 -- 251}, language = {en} } @misc{MissagiaGuerreroNarraetal., author = {Missagia, Bruna and Guerrero, Cinthya and Narra, Satyanarayana and Sun, Yaoming and Ay, Peter and Krautz, Hans Joachim}, title = {Physicomechanical properties of rice husk pellets for energy generation}, language = {en} } @misc{DanilaVollmerKunz, author = {Danila, Kevin and Vollmer, Jan and Kunz, Philip}, title = {Performance evaluation of reanalysis models for upsampling of solar irradiance and wind speed data}, series = {Renewable energy}, volume = {261}, journal = {Renewable energy}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0960-1481}, doi = {10.1016/j.renene.2026.125301}, pages = {1 -- 13}, abstract = {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.}, language = {en} } @misc{AsegehegnSchreiberKrautz, author = {Asegehegn, Teklay Weldeabzgi and Schreiber, Matthias and Krautz, Hans Joachim}, title = {Numerical Study of Bubbling Gas-Solid Fluidized Beds Hydrodynamics: Influence of Immersed Horizontal Tubes and Data Analysis}, language = {en} } @misc{AsegehegnSchreiberKrautz, author = {Asegehegn, Teklay Weldeabzgi and Schreiber, Matthias and Krautz, Hans Joachim}, title = {Numerical simulation and experimental validation of bubble behavior in 2D gas-solid fluidized beds with immersed horizontal tubes}, doi = {http://dx.doi.org/10.1016/j.ces.2011.07.056}, language = {en} } @misc{SchreiberAsegehegnKrautz, author = {Schreiber, Matthias and Asegehegn, Teklay Weldeabzgi and Krautz, Hans Joachim}, title = {Numerical and Experimental Investigation of Bubbling Gas-Solid Fluidized Beds with Dense Immersed Tube Bundles}, language = {en} }