The objective of this study is to evaluate the energetic, exergetic, sustainability, economic and environmental performances of a 4-cylinder turbodiesel aviation engine (TdAE) used on unmanned aerial vehicles for the take-off operation mode to assess the system with large aspects. Energy efficiency of the system is found as 43.158%, while exergy efficiency 40.655%. Thermoeconomic analysis gives information about the costs of the inlet and outlet energy and exergy flows of the engine. Hourly levelized total cost flow of the TdAE is found as 21.036 $/h, when the hourly fuel cost flow of the engine is found as 30.328 $/h. The waste exergy cost parameter is determined as 0.0144 MJ/h/$ from exergy cost-energy-mass (EXCEM) analysis, while it is estimated as 14.043 MJ/$ from modified-EXCEM analysis. Environmental damage cost analysis evaluates the cost formation of the exhaust emissions. The total environmental damage cost of the TdAE is computed as 12.895 $/h whilst specific environmental damage cost is determined as 0.054 $/MJ for 494.145 MJ/h TdAE power production. It is assessed that the main contributors to the environmental impact rate of the TdAE are the fuel consumption and the formation pollutants of combustion reaction.
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The COVID–19 pandemic led to local oxygen shortages worldwide. To gain a better understanding of oxygen consumption with different respiratory supportive therapies, we conducted an international multicenter observational study to determine the precise amount of oxygen consumption with high-flow nasal oxygen (HFNO) and with mechanical ventilation. A retrospective observational study was conducted in three intensive care units (ICUs) in the Netherlands and Spain. Patients were classified as HFNO patients or ventilated patients, according to the mode of oxygen supplementation with which a patient started. The primary endpoint was actual oxygen consumption; secondary endpoints were hourly and total oxygen consumption during the first two full calendar days. Of 275 patients, 147 started with HFNO and 128 with mechanical ventilation. Actual oxygen use was 4.9-fold higher in patients who started with HFNO than in patients who started with ventilation (median 14.2 [8.4–18.4] versus 2.9 [1.8–4.1] L/minute; mean difference 5 11.3 [95% CI 11.0–11.6] L/minute; P, 0.01). Hourly and total oxygen consumption were 4.8-fold (P, 0.01) and 4.8-fold (P, 0.01) higher. Actual oxygen consumption, hourly oxygen consumption, and total oxygen consumption are substantially higher in patients that start with HFNO compared with patients that start with mechanical ventilation. This information may help hospitals and ICUs predicting oxygen needs during high-demand periods and could guide decisions regarding the source of distribution of medical oxygen.
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Within the Flexnode Plus project the long-term degradation characteristics of a proton exchange membrane (PEM) electrolyzer (5.5 kW, AC, 1 Nm3/h H2) and fuel cell (1.0 kW, DC, 0.9 Nm3/h) was experimentally tested. The electrolyzer unit was operated at various loads and pressures for approximately 750 hours in total, while the fuel cell was operated at a constant load of 1 Ω resistance for approximately 1120 hours in total. The efficiency of the hydrogen production in the electrolyzer and the electricity production in the fuel cell was expressed using the hourly average system efficiency and average cell efficiency. Inorder to predict the state of health and remaining lifetime of the electrolyzer cell and fuel cell, the decay of the cell voltage over time was monitored and the direct mapping from aging data method was used.The electrolyzer cell showed a stable cell voltage and cell efficiency in the studied time period, with an average cell voltage decay rate of 0.5 μV/h. The average cell voltage of the fuel cell dropped with a rate of 2 μV/h during the studied time period.
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Background: Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results: We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion: MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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Staatssecretaris Uslu en de Raad voor Cultuur hameren op het belang van fair pay door rijksgesubsidieerde culturele instellingen. Ook in het muziekonderwijs valt nog een wereld te winnen, weet onderzoeker en docentenopleider Imre Kruis. De uurtarieven van muziekdocenten zijn bedroevend laag. ‘Ik schrijf dit in een tijd dat er aan tafels veel gepraat wordt over fair pay. Als het maar niet bij praten blijft, denk ik dan.’
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In this study, aviation, energy, exergy, environmental, exergoeconomic, and exergoenvironmental analyses are performed on a CFM56-3 series high by-pass turbofan engine fueled with Jet-A1 fuel. Specific fuel consumption and specific thrust of the engine are found to be 0.01098 kg/kN.s and 0.3178 kN/kg/s, respectively. Engine's energy efficiency is calculated as 35.37%, while waste energy ratio is obtained as 64.63%. Exergy efficiency, waste exergy rate, and fuel exergy waste ratio are forecasted as 33.32%, 33175.03 kW, and 66.68%, respectively. Environmental effect factor and ecological effect factor are computed as 2.001 and 3.001, while ecological objective function and its index are taken into account of −16597.22 kW and −1.001, respectively. Exergetic sustainability index and sustainable efficiency factor are determined as 0.5 and 1.5 for the CFM56-3 engine, respectively. Environmental damage cost rate is determined as 519.753 $/h, while the environmental damage cost index is accounted as 0.0314 $/kWh. Specific exergy cost of the engine production is found as 40.898 $/GJ from exergoeconomic analysis, while specific product exergy cost is expressed as 49.607 $/GJ from exergoenvironmental analysis. From exergoenvironmental economic analysis, specific exergy cost of fuel is computed as 10.103 $/GJ when specific exergy cost of production is determined as 40.898 $/GJ.
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To reduce greenhouse gas emissions, countries around the world are pursuing electrification policies. In residential areas, electrification will increase electricity supply and demand, which is expected to increase grid congestion at a faster rate than grids can be reinforced. Battery energy storage (BES) has the potential to reduce grid congestion and defer grid reinforcement, thus supporting the energy transition. But, BES could equally exacerbate grid congestion. This leads to the question: What are the trade-offs between different battery control strategies, considering battery performance and battery grid impacts? This paper addresses this question using the battery energy storage evaluation method (BESEM), which interlinks a BES model with an electricity grid model to simulate the interactions between these two systems. In this paper, the BESEM is applied to a case study, wherein the relative effects of different BES control strategies are compared. The results from this case study indicate that batteries can reduce grid congestion if they are passively controlled (i.e., constraining battery power) or actively controlled (i.e., overriding normal battery operations). Using batteries to reduce congestion was found to reduce the primary benefits provided by the batteries to the battery owners, but could increase secondary benefits. Further, passive battery controls were found to be nearly as effective as active battery controls at reducing grid congestion in certain situations. These findings indicate that the trade-offs between different battery control strategies are not always obvious, and should be evaluated using a method like the BESEM.
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Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
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