2016, Windisch, M., Eichhorn, K.-J., Lienig, J., Gerlach, G., Schulze, L.

The volume production of industrial hydrogel sensors lacks a quality-assuring manufacturing technique for thin polymer films with reproducible properties. Overcoming this problem requires a paradigm change from the current recipe-driven manufacturing process to a specification-driven one. This requires techniques to measure quality-determining hydrogel film properties as well as tools and methods for the control and optimization of the manufacturing process. In this paper we present an approach that comprehensively addresses these issues. The influence of process parameters on the hydrogel film properties and the resulting sensor characteristics have been assessed by means of batch manufacturing tests and the application of several measurement techniques. Based on these investigations, we present novel methods and a tool for the optimization of the cross-linking process step, with the latter being crucial for the sensor sensitivity. Our approach is applicable to various sensor designs with different hydrogels. It has been successfully tested with a sensor solution for surface technology based on PVA/PAA hydrogel as sensing layer and a piezoelectric thickness shear resonator as transducer. Finally, unresolved issues regarding the measurement of hydrogel film parameters are outlined for future research.

2020, Blank, Laura, Rioseco, Ernesto Meneses, Caiazzo, Alfonso, Wilbrandt, Ulrich

Geothermal district heating development has been gaining momentum in Europe with numerous deep geothermal installations and projects currently under development. With the increasing density of geothermal wells, questions related to the optimal and sustainable reservoir exploitation become more and more important. A quantitative understanding of the complex thermo-hydraulic interaction between tightly deployed geothermal wells in heterogeneous temperature and permeability fields is key for a maximum sustainable use of geothermal resources. Motivated by the geological settings of the Upper Jurassic aquifer in the Greater Munich region, we develop a computational model based on finite element analysis and gradient-free optimization to simulate groundwater flow and heat transport in hot sedimentary aquifers, and numerically investigate the optimal positioning and spacing of multi-well systems. Based on our numerical simulations, net energy production from deep geothermal reservoirs in sedimentary basins by smart geothermal multi-well arrangements provides significant amounts of energy to meet heat demand in highly urbanized regions. Our results show that taking into account heterogeneous permeability structures and a variable reservoir temperature may drastically affect the results in the optimal configuration. We demonstrate that the proposed numerical framework is able to efficiently handle generic geometrical and geological configurations, and can be thus flexibly used in the context of multi-variable optimization problems. Hence, this numerical framework can be used to assess the extractable geothermal energy from heterogeneous deep geothermal reservoirs by the optimized deployment of smart multi-well systems. © 2020, The Author(s).

2019, Kodalle, T., Greiner, D., Brackmann, V., Prietzel, K., Scheu, A., Bertram, T., Reyes-Figueroa, P., Unold, T., Abou-Ras, D., Schlatmann, R., Kaufmann, C.A., Hoffmann, V.

Determining elemental distributions dependent on the thickness of a sample is of utmost importance for process optimization in different fields e.g. from quality control in the steel industry to controlling doping profiles in semiconductor labs. Glow discharge optical emission spectrometry (GD-OES) is a widely used tool for fast measurements of depth profiles. In order to be able to draw profound conclusions from GD-OES profiles, one has to optimize the measurement conditions for the given application as well as to ensure the suitability of the used emission lines. Furthermore a quantification algorithm has to be implemented to convert the measured properties (intensity of the emission lines versus sputtering time) to more useful parameters, e.g. the molar fractions versus sample depth (depth profiles). In this contribution a typical optimization procedure of the sputtering parameters is adapted to the case of polycrystalline Cu(In,Ga)(S,Se)2 thin films, which are used as absorber layers in solar cell devices, for the first time. All emission lines used are shown to be suitable for the quantification of the depth profiles and a quantification routine based on the assumption of constant emission yield is used. The accuracy of this quantification method is demonstrated on the basis of several examples. The bandgap energy profile of the compound semiconductor, as determined by the elemental distributions, is compared to optical measurements. The depth profiles of Na-the main dopant in these compounds-are correlated with measurements of the open-circuit voltage of the corresponding devices, and the quantification of the sample depth is validated by comparison with profilometry and X-ray fluorescence measurements.

2018, Singh, K., Radhakrishna, A.S., Both, A., Shekarpour, S., Lytra, I., Usbeck, R., Vyas, A., Khikmatullaev, A., Punjani, D., Lange, C., Vidal, Maria-Esther, Lehmann, J., Auer, Sören

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.

2022, Marzban, Nader, Libra, Judy A., Hosseini, Seyyed Hossein, Fischer, Marcus G., Rotter, Vera Susanne

The hydrothermal carbonization (HTC) process has been found to consistently improve biomass fuel characteristics by raising the higher heating value (HHV) of the hydrochar as process severity is increased. However, this is usually associated with a decrease in the solid yield (SY) of hydrochar, making it difficult to determine the optimal operating conditions to obtain the highest energy yield (EY), which combines the two parameters. In this study, a graph-based genetic programming (GP) method was used for developing correlations to predict HHV, SY, and EY for hydrochars based on published values from 42 biomasses and a broad range of HTC experimental systems and operating conditions, i.e., 5 ≤ holding time (min) ≤ 2208, 120 ≤ temperature (°C) ≤ 300, and 0. 0096 ≤ biomass to water ratio ≤ 0.5. In addition, experiments were carried out with 5 pomaces at 4 temperatures and two reactor scales, 1 L and 18.75 L. The correlations were evaluated using this experimental data set in order to estimate prediction errors in similar experimental systems. The use of the correlations to predict HTC conditions to achieve the maximum EY is demonstrated for three common feedstocks, wheat straw, sewage sludge, and a fruit pomace. The prediction was confirmed experimentally with pomace at the optimized HTC conditions; we observed 6.9 % error between the measured and predicted EY %. The results show that the correlations can be used to predict the optimal operating conditions to produce hydrochar with the desired fuel characteristics with a minimum of actual HTC runs.