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In the course of the last few years, our students are becoming increasingly unhappy. Sometimes they stop attending lectures and even seem not to know how to behave correctly. It feels like they are getting on strike. Consequently, drop-out rates are sky-rocketing. The lecturers/professors are not happy either, adopting an “I-don’t-care” attitude.
An interdisciplinary, international team set in to find out: (1) What are the students unhappy about? Why is it becoming so difficult for them to cope? (2) What does the “I-don’t-care” attitude of professors actually mean? What do they care or not care about? (3) How far do the views of the parties correlate? Could some kind of mutual understanding be achieved?
The findings indicate that, at least at our universities, there is rather a long way to go from “Engineering versus Pedagogy” to “Engineering Pedagogy”.
Three real-lab trigeneration microgrids are investigated in non-residential environments (educational, office/administrational, companies/production) with a special focus on domain-specific load characteristics. For accurate load forecasting on such a local level, à priori information on scheduled events have been combined with statistical insight from historical load data (capturing information on not explicitly-known consumer behavior). The load forecasts are then used as data input for (predictive) energy management systems that are implemented in the trigeneration microgrids. In real-world applications, these energy management systems must especially be able to carry out a number of safety and maintenance operations on components such as the battery (e.g. gassing) or CHP unit (e.g. regular test runs). Therefore, energy management systems should combine heuristics with advanced predictive optimization methods. Reducing the effort in IT infrastructure the main and safety relevant management process steps are done on site using a Smart & Local Energy Controller (SLEC) assisted by locally measured signals or operator given information as default and external inputs for any advanced optimization. Heuristic aspects for local fine adjustment of energy flows are presented.
Polygeneration systems are a key technology for the reduction of primary energy usage and emissions. High costs, lack of flexibility and effort for parameterization hinder the wide usage of modeling tools during their conceptual design. This paper describes how planning tools can be structured for the conceptual design phase where only little information is available to the planner. A library concept was developed using the principles of object-oriented modeling to address the flexibility issue. With respect to cost and expandability, the open-source modeling language Modelica was chosen. Furthermore, easy-to-parameterize component models were developed. In addition to the improved library concept and novel component models, an easy-to-adapt control concept is proposed. The component models were validated and the applicability of the library was demonstrated by means of an example. It was shown that the data usually obtained from spec sheets are sufficient to parameterize the models. In addition to this, the control concept was approved.
Radiation is an important means of heat transfer inside an electric arc furnace (EAF).
To gain insight into the complex processes of heat transfer inside the EAF vessel, not only radiation from the surfaces but also emission and absorption of the gas phase and the dust cloud need to be considered.
Furthermore, the radiative heat exchange depends on the geometrical configuration which is continuously changing throughout the process.
The present paper introduces a system model of the EAF which takes into account the radiative heat transfer between the surfaces
and the participating medium. This is attained by the development of a simplified geometrical model,
the use of a weighted-sum-of-gray-gases model, and a simplified consideration of dust radiation.
The simulation results were compared with the data of real EAF plants available in literature.
Battery degradation is a complex physicochemical process that strongly depends on operating conditions. We present a model-based analysis of lithium-ion battery degradation in a stationary photovoltaic battery system. We use a multi-scale multi-physics model of a graphite/lithium iron phosphate (LiFePO4, LFP) cell including solid electrolyte interphase (SEI) formation. The cell-level model is dynamically coupled to a system-level model consisting of photovoltaics (PV), inverter, load, grid interaction, and energy management system, fed with historic weather data. Simulations are carried out for two load scenarios, a single-family house and an office tract, over annual operation cycles with one-minute time resolution. As key result, we show that the charging process causes a peak in degradation rate due to electrochemical charge overpotentials. The main drivers for cell ageing are therefore not only a high state of charge (SOC), but the charging process leading towards high SOC. We also show that the load situation not only influences system parameters like self-sufficiency and self-consumption, but also has a significant impact on battery ageing. We assess reduced charge cut-off voltage as ageing mitigation strategy.
The DMFC is a promising option for backup power systems and for the power supply of portable devices. However, from the modeling point of view liquid-feed DMFC are challenging systems due to the complex electrochemistry, the inherent two-phase transport and the effect of methanol crossover. In this paper we present a physical 1D cell model to describe the relevant processes for DMFC performance ranging from electrochemistry on the surface of the catalyst up to transport on the cell level. A two-phase flow model is implemented describing the transport in gas diffusion layer and catalyst layer at the anode side. Electrochemistry is described by elementary steps for the reactions occurring at anode and cathode, including adsorbed intermediate species on the platinum and ruthenium surfaces. Furthermore, a detailed membrane model including methanol crossover is employed. The model is validated using polarization curves, methanol crossover measurements and impedance spectra. It permits to analyze both steady-state and transient behavior with a high level of predictive capabilities. Steady-state simulations are used to investigate the open circuit voltage as well as the overpotentials of anode, cathode and electrolyte. Finally, the transient behavior after current interruption is studied in detail.
We present a two-dimensional (2D) planar chromatographic separation of estrogenic active compounds on RP-18 W (Merck, 1.14296) phase. A mixture of 8 substances was separated using a solvent mix consisting of hexane, ethyl acetate, acetone (55:15:10, v/v) in the first direction and of acetone and water (15:10, v/v) in the second direction. Separation was performed on an RP-18 W plate over a distance of 70 mm. This 2D-separation method can be used to quantify 17α-ethinylestradiol (EE2) in an effect-directed analysis, using the yeast strain Saccharomyces cerevisiae BJ3505. The test strain (according to McDonnell) contains the estrogen receptor. Its activation by estrogen active compounds is measured by inducing the reporter gene lacZ which encodes the enzyme β-galactosidase. This enzyme activity is determined on plate by using the fluorescent substrate MUG (4-methylumbelliferyl-β-d-galactopyranoside).
Modelling and Simulation of Microscale Trigeneration Systems Based on Real- Life Experimental Data
(2017)
For the shift of the energy grid towards a smarter decentralised system flexible microscale trigeneration systems will play an important role due to their ability to support the demand side management in buildings. However to harness their potential modern control methods like model predictive control must be implemented for their optimal scheduling and control. To implement such supervisory control methods, first, simple analytical models representing the behaviour of the components need to be developed. At the Institute of Energy System Technologies in Offenburg we have built a real-life microscale trigeneration plant and present in this paper the models based on experimental data. These models are qualitatively validated and their application in the future for the optimal scheduling problem is briefly motivated.