Thermo-Economic Analysis of Zeotropic Mixtures and Pure Working Fluids in Organic Rankine Cycles for Waste Heat Recovery

We present a thermo-economic analysis of an Organic Rankine Cycle (ORC) for waste heat recovery. A case study for a heat source temperature of 150 °C and a subcritical, saturated cycle is performed. As working fluids R245fa, isobutane, isopentane, and the mixture of isobutane and isopentane are considered. The minimal temperature difference in the evaporator and the condenser, as well as the mixture composition are chosen as variables in order to identify the most suitable working fluid in combination with optimal process parameters under thermo-economic criteria. In general, the results show that cost-effective systems have a high minimal temperature difference ΔTPP,C at the pinch-point of the condenser and a low minimal temperature difference ΔTPP,E at the pinch-point of the evaporator. Choosing isobutane as the working fluid leads to the lowest costs per unit exergy with 52.0 €/GJ (ΔTPP,E = 1.2 K; ΔTPP,C = 14 K). Considering the major components of the ORC, specific costs range between 1150 €/kW and 2250 €/kW. For the zeotropic mixture, a mole fraction of 90% isobutane leads to the lowest specific costs per unit exergy. A further analysis of the ORC system using isobutane shows high sensitivity of the costs per unit exergy for the selected cost estimation methods and for the isentropic efficiency of the turbine

Thermoeconomic Analysis of Hybrid Power Plant Concepts for Geothermal Combined Heat and Power Generation

We present a thermo-economic analysis for a low-temperature Organic Rankine Cycle (ORC) in a combined heat and power generation (CHP) case. For the hybrid power plant, thermal energy input is provided by a geothermal resource coupled with the exhaust gases of a biogas engine. A comparison to alternative geothermal CHP concepts is performed by considering variable parameters like ORC working fluid, supply temperature of the heating network or geothermal water temperature. Second law efficiency as well as economic parameters show that hybrid power plants are more efficient compared to conventional CHP concepts or separate use of the energy sources

Experimental Long-Term Investigation of Model Predictive Heat Pump Control in Residential Buildings with Photovoltaic Power Generation

This article presents a 125-day experiment to investigate model predictive heat pump control. The experiment was performed in two parallel operated systems with identical components during the heating season. One of the systems was operated by a standard controller and thus represented a reference to evaluate the model predictive control. Both test rigs were heated by an air-source heat pump which is influenced by real weather conditions. A single-family house model depending on weather measurement data ensured a realistic heat consumption in the test rigs. The adapted model predictive control algorithm aimed to minimize the operational costs of the heat pump. The evaluation of the measurement results showed that the electrical energy demand of the heat pump can be reduced and the coefficient of performance can be increased by applying the model predictive controller. Furthermore, the self-consumption of photovoltaic electricity, which is calculated by means of a photovoltaic model and global radiation measurement data, was more than doubled. Consequently, the energy costs of heat pump operation were reduced by 9.0% in comparison to the reference and assuming German energy prices. The results were further compared to the scientific literature and short-term measurements were performed with the same experimental setup. The dependence of the measurement results on the weather conditions and the weather forecasting quality are shown. It was found that the duration of experiments should be as long as possible for a comprehensive evaluation of the model predictive control potential

The Geometry of the Catalytic Active Site in [FeFe]-hydrogenases is Determined by Hydrogen Bonding and Proton Transfer

[FeFe]-hydrogenases are efficient metalloenzymes that catalyze the oxidation and evolution of molecular hydrogen, H2. They serve as a blueprint for the design of synthetic H2-forming catalysts. [FeFe]-hydrogenases harbor a six-iron cofactor that comprises a [4Fe-4S] cluster and a unique diiron site with cyanide, carbonyl, and hydride ligands. To address the ligand dynamics in catalytic turnover and upon carbon monoxide (CO) inhibition, we replaced the native aminodithiolate group of the diiron site by synthetic dithiolates, inserted into wild-type and amino acid variants of the [FeFe]-hydrogenase HYDA1 from Chlamydomonas reinhardtii. The reactivity with H2 and CO was characterized using in situ and transient infrared spectroscopy, protein crystallography, quantum chemical calculations, and kinetic simulations. All cofactor variants adopted characteristic populations of reduced species in the presence of H2 and showed significant changes in CO inhibition and reactivation kinetics. Differences were attributed to varying interactions between polar ligands and the dithiolate head group and/or the environment of the cofactor (i.e., amino acid residues and water molecules). The presented results show how catalytically relevant intermediates are stabilized by inner-sphere hydrogen bonding suggesting that the role of the aminodithiolate group must not be restricted to proton transfer. These concepts may inspire the design of improved enzymes and biomimetic H2-forming catalysts

Personalized web learning: merging Open Educational Resources into adaptive courses for higher education

In this paper, educational and technical challenges for applying learning pathways in Massive(ly) Open Online Courses in higher education are outlined. The authors argue that quality issues and didactical concerns may be overcome by (1) reverting to small Open Educational Resources that are (2) adaptively joined into concise courses by considering (3) predefined learning pathways with proper semantic annotations and (4) the observation of learner behaviour. Such a merger does not only require conceptual work and corresponding support tools, but also a new meta data format and an engine which interprets the semantic annotations as well as the measures of learner’s actions. These factors are then turned into didactically meaningful recommendations for the next learning steps, thereby creating a personalized learning pathway for each learner. The EU FP7 project INTUITEL is introduced, which has already contributed to the conceptual work and is currently developing the software to achieve these tasks. (DIPF/Orig.

Stepwise isotope editing of [FeFe]-hydrogenases exposes cofactor dynamics

The six-iron cofactor of [FeFe]-hydrogenases (H-cluster) is the most efficient H2-forming catalyst in nature. It comprises a diiron active site with three carbon monoxide (CO) and two cyanide (CN−) ligands in the active oxidized state (Hox) and one additional CO ligand in the inhibited state (Hox-CO). The diatomic ligands are sensitive reporter groups for structural changes of the cofactor. Their vibrational dynamics were monitored by real-time attenuated total reflection Fourier-transform infrared spectroscopy. Combination of 13CO gas exposure, blue or red light irradiation, and controlled hydration of three different [FeFe]-hydrogenase proteins produced 8 Hox and 16 Hox-CO species with all possible isotopic exchange patterns. Extensive density functional theory calculations revealed the vibrational mode couplings of the carbonyl ligands and uniquely assigned each infrared spectrum to a specific labeling pattern. For Hox-CO, agreement between experimental and calculated infrared frequencies improved by up to one order of magnitude for an apical CN− at the distal iron ion of the cofactor as opposed to an apical CO. For Hox, two equally probable isomers with partially rotated ligands were suggested. Interconversion between these structures implies dynamic ligand reorientation at the H-cluster. Our experimental protocol for site-selective 13CO isotope editing combined with computational species assignment opens new perspectives for characterization of functional intermediates in the catalytic cycle

fluorination, density, roughness, and Lennard-Jones cutoffs

The interplay of fluorination and structure of alkane self-assembled monolayers and how these affect hydrophobicity are explored via molecular dynamics simulations, contact angle goniometry, and surface-enhanced infrared absorption spectroscopy. Wetting coefficients are found to grow linearly in the monolayer density for both alkane and perfluoroalkane monolayers. The larger contact angles of monolayers of perfluorinated alkanes are shown to be primarily caused by their larger molecular volume, which leads to a larger nearest-neighbor grafting distance and smaller tilt angle. Increasing the Lennard-Jones force cutoff in simulations is found to increase hydrophilicity. Specifically, wetting coefficients scale like the inverse square of the cutoff, and when extrapolated to the infinite cutoff limit, they yield contact angles that compare favorably to experimental values. Nanoscale roughness is also found to reliably increase monolayer hydrophobicity, mostly via the reduction of the entropic part of the work of adhesion. Analysis of depletion lengths shows that droplets on nanorough surfaces partially penetrate the surface, intermediate between Wenzel and Cassie–Baxter states