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Complexes like iron (II)-triazoles exhibit spin crossover behavior at ambient temperature and are often considered for possible application. In previous studies, we implemented complexes of this type into polymer nanofibers and first polymer-based optical waveguide sensor systems. In our current study, we synthesized complexes of this type, implemented them into polymers and obtained composites through drop casting and doctor blading. We present that a certain combination of polymer and complex can lead to composites with high potential for optical devices. For this purpose, we used two different complexes [Fe(atrz)3](2 ns)2 and [Fe(atrz)3]Cl1.5(BF4)0.5 with different polymers for each composite. We show through transmission measurements and UV/VIS spectroscopy that the optical properties of these composite materials can reversibly change due to the spin crossover effect.
Compounds that exhibit the spin crossover effect are known to show a change of spin states through external stimuli. This reversible switching of spin states is accompanied by a change of the properties of the compound. Complexes, like iron (II)-triazole complexes, that exhibit this behavior at ambient temperature are often discussed for potential applications. In previous studies we synthesized iron (II)-triazole complexes and implemented them into electrospun nanofibers. We used Mössbauer spectroscopy in first studies to prove a successful implementation with maintaining spin crossover properties. Further studies from us showed that it is possible to use different electrospinning methods to either do a implementation or a deposition of the synthesized solid SCO material into or onto the polymer nanofibers. We now used a solvent in which both, the used iron (II)-triazole complex [Fe(atrz)3](2 ns)2 and three different polymers (Polyacrylonitrile, Polymethylmethacrylate and Polyvinylpyrrolidone), are soluble. This shall lead to a higher homogeneous distribution of the complex along the nanofibers. Mössbauer spectroscopy and other measurements are therefore in use to show a successful implementation without any significant changes to the complex.
During the transition from conventional towards purely electrical, sustainable mobility, transitional technologies play a major part in the task of increasing adaption rates and decreasing range anxiety. Developing new concepts to meet this challenge requires adaptive test benches, which can easily be modified e.g. when progressing from one stage of development to the next, but also meet certain sustainability demands themselves.
The system architecture presented in this paper is built around a service-oriented software layer, connecting a modular hardware layer for direct access to sensors and actuators to an extensible set of client tools. Providing flexibility, serviceability and ease of use, while maintaining a high level of reusability for its constituent components and providing features to reduce the required overall run time of the test benches, it can effectively decrease the CO2 emissions of the test bench while increasing its sustainability and efficiency.
This paper presents the fundamental investigation on crack propagation rate (CPR) and Stress Intensity Factor (SIF) for a typical fatigue and welded specimens which are Compact Tension (CT) and Single Edge Notch Tension (SENT) as well as Butt and longitudinal T-joint. The material data of austenitic stainless steel SS316L was used to observe crack propagation rate with different initial crack length and different tensile load was used for the fracture mechanics investigation. The geometry of the specimens was modelled by using open source software CASCA while Franc 2D was used for post processing based on Paris Erdogan Law with different crack increment steps. The analysis of crack propagation using fracture mechanics technique requires an accurate calculation of the stress intensity factor SIF and comparison of the critical strength of the material (KIC) was used to determine the critical crack length of the specimens. it can be concluded that open source finite element method software can be used for predicting of fatigue life on simplified geometry.
The increasing variety of combinations of different building technology components offers a high potential for energy and cost savings in today's buildings. However, in most cases, this potential is not yet fully exploited due to the lack of intelligent supervisory control systems that are required to manage the complexity of the resulting overall systems. In this article, we present the implementation of a mixed-integer nonlinear model predictive control approach as a smart realtime building energy management system. The presented methodology is based on a forward-looking optimization of the overall energy costs. It takes into account energy demand forecasts and varying electricity market prices. We achieve real-time capability of the controller by applying a decomposition approach, which approximates the optimal solution of the underlying mixed-integer optimal control problem by convexification and rounding of the relaxed solution. The quality of the suboptimal solution is evaluated by comparison with the globally optimal solution obtained by the dynamic programming method. Based on a real-world scenario, we demonstrate that utilization of the real-time capable mixedinteger nonlinear model predictive control approach in a building control system leads to savings of 16% in the total operating costs and 13% in primary energy compared to the state-of-the-art control strategy without any loss of comfort for the residents.
This paper presents a novel approach for modelling the energy consumption of the coupled parallel moulding sand mixers of a foundry as an optimal control problem. The minimization of energy consumption is optimized by scheduling the mixing processes in a linear integer programming scheme. The sand flow through the foundry’s sand preparation is characterized by a physical model. This model considers the sand demand of the moulding machine as disturbance, the stored sand masses in the mixer hoppers and machine hoppers, respectively. The novel approach of handling dwell-times for dosing, mixing and transport processes using dead-time systems and constraint pushing allows the application of a linear model. The formulation of the optimal control problem aims at real-time application as model predictive control at the production plant. Initial application results indicate an improvement in energy consumption of approximately 8%.
Da mit wachsender Verwendung von Kunststoffen für konstruktive Anwendungen deren Klebbarkeit immer größere Bedeutung erhält und Vorbehandlungen mit umweltbelastenden Verfahren nur mit Vorbehalt eigesetzt werden können, dürften umweltfreundliche Verbehandlungen, wie die Vorbehandlung im Niederdruckplasma, im Folgenden kurz NDP-Vorbehandlung genannt, eine steigende Bedeutung erlangen. Die Umweltfreundlichkeit des Verfahrens ist darin begründet, dass keine verbrauchten Beizbäder anfallen, die mit hohem Aufwand beseitigt werden müssen, und dass der Prozess in einem geschlossenem System abläuft und somit ein unkontrolliertes Entweichen von Schadstoffen nicht möglich ist. Bei der Ndp-Behandlung gegebenenfalls entstehende Schadstoffe fallen nur in sehr geringem Umfang an und können leicht abgefangen und nachbehandelt werden. Da die verwendeten Gase nicht giftig sind, geht von ihnen keine Gefährdung aus.
We present a novel long short-term memory (LSTM) approach for time-series prediction of the sand demand which arises from preparing the sand moulds for the iron casting process of a foundry. With our approach, we contribute to qualify LSTM and its combination with feedback-corrected optimal scheduling for industrial processes.
The sand is produced in an energy intensive mixing process which is controlled by optimal scheduling. The optimal scheduling is solved for a fixed prediction horizon. One major influencing factor is the sand demand, which is highly disturbed, for example due to production interruptions. The causes of production interruptions are in general physically unknown. We assume that information about the future behavior of the sand demand is included in current and past process data. Therefore, we choose LSTM networks for predicting the time-series of the sand demand.
The sand demand prediction is performed by our multi model approach. This approach outperforms the currently used naive estimation, even when predicting far into the future. Our LSTM based prediction approach can forecast the sand demand with a conformity up to 38 % and a mean value accuracy of approximately 99%. Simulating the optimal scheduling with sand demand prediction leads to an improvement in energy savings of approximately 1.1% compared to the naive estimation. The application of our novel approach at the real production plant of a foundry proves the simulation results and verifies the capability of our approach.