Methanation - Pilot Plant ADAM I (NFE Proiect) and other Methanation Pilot Plants

According to tOday' s energy forecasts. the energy market requires a considerable percentage of low temperature energy, and NUCLEAR LONG-DISTANCE ENERGY represents a promising alternative source for this energy. In connection with nuclear coal gasification, the NUCLEAR LONGDISTANCE ENERGY SYSTEM provides an open gas circuit system for both the transportation of chemical energy and the supply of raw gas for various purposes. In this context. it appears to be reasonable to consider a cyclic system with a high temperature nuclear reactor as heat source for the methane steam reforming process at one side and the methanation process with heat utilization at the other. [...

IEC 60296 (Ed. 5) – a standard for classification of mineral insulating oil on performance and not on the origin

The revision of the standard IEC 60296, Ed. 4.0 resulting in IEC 60296, Ed. 5.0 had three main aims: to set up a standard based on the performance of mineral insulating oil and not on the origin, to distinguish between good and bad mineral insulating oils, and to protect the user providing adequate testing parameters. In addition, there are several exciting news and changes in the new version of the standard compared to the previous versions

Thermophysical Characterization of MgCl2·6H2O, Xylitol and Erythritol as Phase Change Materials (PCM) for Latent Heat Thermal Energy Storage (LHTES)

The application range of existing real scale mobile thermal storage units with phase change materials (PCM) is restricted by the low phase change temperature of 58 ∘ C for sodium acetate trihydrate, which is a commonly used storage material. Therefore, only low temperature heat sinks like swimming pools or greenhouses can be supplied. With increasing phase change temperatures, more applications like domestic heating or industrial process heat could be operated. The aim of this study is to find alternative PCM with phase change temperatures between 90 and 150 ∘ C . Temperature dependent thermophysical properties like phase change temperatures and enthalpies, densities and thermal diffusivities are measured for the technical grade purity materials xylitol (C 5 H 12 O 5 ), erythritol (C 4 H 10 O 4 ) and magnesiumchloride hexahydrate (MCHH, MgCl 2 · 6H 2 O). The sugar alcohols xylitol and erythritol indicate a large supercooling and different melting regimes. The salt hydrate MgCl 2 · 6H 2 O seems to be a suitable candidate for practical applications. It has a melting temperature of 115.1 ± 0.1 ∘ C and a phase change enthalpy of 166.9 ± 1.2 J / g with only 2.8 K supercooling at sample sizes of 100 g . The PCM is stable over 500 repeated melting and solidification cycles at differential scanning calorimeter (DSC) scale with only small changes of the melting enthalpy and temperature

Neue Energieträger für den Verkehr: Methanol und Alkoholgemische

Traffic contributes considerably to environmental pollution in the Federal Republic of Germany: today's fuels are produced on the basis of oil as a fossil primary energy carrier, and these fuels are burnt at low efficiency, releasing particularly noxious emissions. Thus, there is good reason to suppose that the situation should be improved by reducing these ecologically harmful emissions to a minimum. This report concentrates on the improvement potential of the traffic system with regard to liquid energy carriers used in road traffic. As compared to the present situation with gasoline and diesel used as energy carriers, it is suggested to introduce methanol and alcohol mixtures as liquid synthetic energy carriers. Methanol and alcohol mixtures are preferable to other possible solutions in that on the one hand, their usage in the road traffic implies only minor overall changes as to transport and storage conditions, service stations, and vehicle technology; on the other hand, they provide major environmental advantages. The R&D work of the Research center Jülich (KFA) described in this report includes laboratory tests (synthesis of alcohol mixtures and novel methanol syntheses), pilot plant tests (synthesis of alcohols), technological process developments, and the study of both environmental aspects and the political instruments required for introducing methanol and alcohol mixtures in the market

Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles

We present the first neural network that has learned to compactly represent and can efficiently reconstruct the statistical dependencies between the values of physical variables at different spatial locations in large 3D simulation ensembles. Going beyond linear dependencies, we consider mutual information as a measure of non-linear dependence. We demonstrate learning and reconstruction with a large weather forecast ensemble comprising 1000 members, each storing multiple physical variables at a 250 x 352 x 20 simulation grid. By circumventing compute-intensive statistical estimators at runtime, we demonstrate significantly reduced memory and computation requirements for reconstructing the major dependence structures. This enables embedding the estimator into a GPU-accelerated direct volume renderer and interactively visualizing all mutual dependencies for a selected domain point

Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks

Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In contrast to previous approaches, which often operate on ensemble summary statistics and dismiss details of the ensemble distribution, we propose networks which treat forecast ensembles as a set of unordered member forecasts and learn link functions that are by design invariant to permutations of the member ordering. We evaluate the quality of the obtained forecast distributions in terms of calibration and sharpness, and compare the models against classical and neural network-based benchmark methods. In case studies addressing the postprocessing of surface temperature and wind gust forecasts, we demonstrate state-of-the-art prediction quality. To deepen the understanding of the learned inference process, we further propose a permutation-based importance analysis for ensemble-valued predictors, which highlights specific aspects of the ensemble forecast that are considered important by the trained postprocessing models. Our results suggest that most of the relevant information is contained in few ensemble-internal degrees of freedom, which may impact the design of future ensemble forecasting and postprocessing systems.Comment: Submitted to Artificial Intelligence for the Earth System

Development and screening of selective catalysts for the synthesis of clean liquid fuels

This article is a compilation of the research carried out under EEC contract EN3V-0400-D at the Institut für Energieverfahrenstechnik in Jülich and at the Faculty of Chemical Technology and Materials Science, Delft, concerning the development and screening of copper/cobalt-based catalysts for the synthesis of alcohol mixtures from syngas. Analogous work, based on copper/zinc oxide/alumina catalysts, has been performed at the Faculty of Chemical Technology in Twente University at Enschede. This work is described in a companion paper. Comparative tests of several catalysts in a pressure micropulse reactor and in a plug flow tubular reactor, carried out at the Institut für Technische Chemie, TU Braunschweig, are presented in a second companion paper. \ud In the discussion section of the present article the results obtained by the joint groups are compared with the initial objectives of the programme

An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to emerge from - the data mining process. We will first validate this emergent space reconstruction for time series sampled without space labels in known PDEs; this brings up the issue of observability of physical space from temporal observation data, and the transition from spatially resolved to lumped (order-parameter-based) representations by tuning the scale of the data mining kernels. We will then present actual emergent space discovery illustrations. Our illustrative examples include chimera states (states of coexisting coherent and incoherent dynamics), and chaotic as well as quasiperiodic spatiotemporal dynamics, arising in partial differential equations and/or in heterogeneous networks. We also discuss how data-driven spatial coordinates can be extracted in ways invariant to the nature of the measuring instrument. Such gauge-invariant data mining can go beyond the fusion of heterogeneous observations of the same system, to the possible matching of apparently different systems

Macro-Encapsulation of Inorganic Phase-Change Materials (PCM) in Metal Capsules

The design of phase-change material (PCM)-based thermal energy storage (TES) systems is challenging since a lot of PCMs have low thermal conductivities and a considerable volume change during phase-change. The low thermal conductivity restricts energy transport due to the increasing thermal resistance of the progressing phase boundary and hence large heat transfer areas or temperature differences are required to achieve sufficient storage power. An additional volume has to be considered in the storage system to compensate for volume change. Macro-encapsulation of the PCM is one method to overcome these drawbacks. When designed as stiff containers with an air cushion, the macro-capsules compensate for volume change of the PCM which facilitates the design of PCM storage systems. The capsule walls provide a large surface for heat transfer and the thermal resistance is reduced due to the limited thickness of the capsules. Although the principles and advantages of macro-encapsulation have been well known for many years, no detailed analysis of the whole encapsulation process has been published yet. Therefore, this research proposes a detailed development strategy for the whole encapsulation process. Various possibilities for corrosion protection, fill and seal strategies and capsule geometries are studied. The proposed workflow is applied for the encapsulation of the salt hydrate magnesiumchloride hexahydrate (MCHH, MgCl 2 · 6 H 2 O) within metal capsules but can also be assigned to other material combinations