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Background: The environmental impact of electric scooters has been the subject of critical debate in the scientific community for the past 5 years. The data published so far are very inhomogeneous and partly methodologically incomplete. Most of the data available in the literature suffer from an average bias of 34%, because end-of-life (EOL) impacts have not been modelled, reported or specified. In addition, the average lifetime mileage of shared fleets of e-scooters, as they are operated in cities around the world, has recently turned out to be much lower than expected. This casts the scooters in an unfavourable light for the necessary mobility transition. Data on impact categories other than the global warming potential (GWP) are scarce. This paper aims to quantify the strengths and weaknesses of e-scooters in terms of their contribution to sustainable transport by more specifically defining and extending the life cycle assessment (LCA) modelling conditions: the modelling is based on two genuine material inventories obtained by dismantling two different e-scooters, one based on a traditional aluminium frame and another, for the first time, based on plastic material.
Results: This study provides complete inventory data to facilitate further LCA modelling of electric kick scooters. The plastic scooter had a 26% lower lifetime GWP than the aluminium vehicle. A favourable choice of electric motor promises a further reduction in GWP. In addition to GWP, the scooter's life cycles were assessed across seven other impact categories and showed no critical environmental or health impacts compared to a passenger car. On the other hand, only the resource extraction impact revealed clear advantages for electric scooters compared to passenger cars.
Conclusions: Under certain conditions, scooters can still be an important element of the desired mobility transition. To assure a lifetime long enough is the crucial factor to make the electric scooter a favourable or even competitive vehicle in a future sustainable mobility system. A scooter mileage of more than 5400 km is required to achieve lower CO2eq/pkm emissions compared to passenger cars, which seems unlikely in today's standard use case of shared scooter fleets. In contrast, a widespread use of e-scooters as a commuting tool is modelled to be able to save 4% of greenhouse gas (GHG) emissions across the German mobility sector.
Additive manufacturing is an essential tool in innovative production processes. The extended degrees of freedom offer much potential in usage, construction, and product design. Rising raw material and energy costs, constantly increasing environmental requirements, and the increasing demand for resource-saving products represent a paradigm shift in classic production processes.
In addition to the purely energetic evaluation, developing energy models is a method to determine energy consumption and reduce it in the long term. The specific energy consumption model, also known as the SEC model, allows a quick estimation of energy consumption by multiplying the SEC with a unit like the mass of the workpiece, the manufacturing time, or the exposed area. Here, high dependence on the used machine, the considered peripheral devices, and the geometry are noticeable.
Previous studies, such as those by Kellens et al. and Baumers et al., have laid the basis for understanding the energy demands of PBF-LB/M processes. Various energy models have subsequently been proposed, including those by Paul and Anand, Yi et al., Lv et al., and Hui et al. These models are often limited by their specificity to sub-processes or subsystems. This results in limitations in their applicability to other manufacturing machines or inaccuracies in energy consumption predictions. The simulation accuracy ACC is mostly in the range of 90% with the limitation of small sample sizes. Moreover, nearly, all these models rely heavily on process time information, making the accuracy of their simulations largely dependent on the quality of the underlying time model.
In the following study, two manufacturing machines of the PBF-LB/M process are analyzed and compared with other studies. The aim is to analyze the power and resource consumption to use these data to build an improved energy model with a high accuracy, which can be used as an additional parameter in the adapted design methodology. Furthermore, potential savings are derived from the load curves.
This research conducted a probabilistic life-cycle assessment (pLCA) into the greenhouse gas (GHG) emissions performance of nine combinations of truck size and powertrain technology for a recent past and a future (largely decarbonised) situation in Australia. This study finds that the relative and absolute life-cycle GHG emissions performance strongly depends on the vehicle class, powertrain and year of assessment. Life-cycle emission factor distributions vary substantially in their magnitude, range and shape. Diesel trucks had lower life-cycle GHG emissions in 2019 than electric trucks (battery, hydrogen fuel cell), mainly due to the high carbon-emission intensity of the Australian electricity grid (mainly coal) and hydrogen production (mainly through steam–methane reforming). The picture is, however, very different for a more decarbonised situation, where battery electric trucks, in particular, provide deep reductions (about 75–85%) in life-cycle GHG emissions. Fuel-cell electric (hydrogen) trucks also provide substantial reductions (about 50–70%), but not as deep as those for battery electric trucks. Moreover, hydrogen trucks exhibit the largest uncertainty in emissions performance, which reflects the uncertainty and general lack of information for this technology. They therefore carry an elevated risk of not achieving the expected emission reductions. Battery electric trucks show the smallest (absolute) uncertainty, which suggests that these trucks are expected to deliver the deepest and most robust emission reductions. Operational emissions (on-road driving and vehicle maintenance combined) dominate life-cycle emissions for all vehicle classes. Vehicle manufacturing and upstream emissions make a relatively small contribution to life-cycle emissions from diesel trucks (<5% each), but these are important aspects for electric trucks (5% to 30%).
Die Umbenetzungsagglomeration bietet die Möglichkeit einer Trennung nach zwei Partikeleigenschaften. Hierbei wird nach den Benetzungseigenschaften des Feststoffs in Bezug zur Suspensions- und Bindeflüssigkeit getrennt und nach der Größe. Ziel dieser Arbeit ist, die Reinheit der entstehenden Graphitagglomerate in einer Mischung mit Quarzsand gravimetrisch zu bestimmen und diese mit den Benetzungseigenschaften zu korrelieren. Die Güte dieser Ergebnisse wird mit einer Mikroröntgentomographie (µ-CT) untersucht. Es konnte gezeigt werden, dass sich Quarzsand mit Paraffinöl nicht benetzen lässt und somit Reinheiten von 99,5 % bis 99,9 % erreicht werden, was das Ergebnis der µ-CT bestätigt. Einen Einfluss der Partikelgröße des Quarzsandes konnte im untersuchten Bereich nicht bestätigt werden.
The number of additive manufacturing methods and materials is growing rapidly, leaving gaps in the knowledge of specific material properties. A relatively recent addition is the metal-filled filament to be printed similarly to the fused filament fabrication (FFF) technology used for plastic materials, but with additional debinding and sintering steps. While tensile, bending, and shear properties of metals manufactured this way have been studied thoroughly, their fatigue properties remain unexplored. Thus, the paper aims to determine the tensile, fatigue, and impact strengths of Markforged 17-4 PH and BASF Ultrafuse 316L stainless steel to answer whether the metal FFF can be used for structural parts safely with the current state of technology. They are compared to two 316L variants manufactured via selective laser melting (SLM) and literature results. For extrusion-based additive manufacturing methods, a significant decrease in tensile and fatigue strength is observed compared to specimens manufactured via SLM. Defects created during the extrusion and by the pathing scheme, causing a rough surface and internal voids to act as local stress risers, handle the strength decrease. The findings cast doubt on whether the metal FFF technique can be safely used for structural components; therefore, further developments are needed to reduce internal material defects.
While the contribution of renewable energy technologies to the energy system is increasing, so is its level of complexity. In addition to new types of consumer systems, the future system will be characterized by volatile generation plants that will require storage technologies. Furthermore, a solid interconnected system that enables the transit of electrical energy can reduce the need for generation and storage systems. Therefore, appropriate methods are needed to analyze energy production and consumption interactions within different system constellations. Energy system models can help to understand and build these future energy systems. However, although various energy models already exist, none of them can cover all issues related to integrating renewable energy systems. The existing research gap is also reflected in the fact that current models cannot model the entire energy system for very high shares of renewable energies with high temporal resolution (15 min or 1-h steps) and high spatial resolution. Additionally, the low availability of open-source energy models leads to a lack of transparency about exactly how they work. To close this gap, the sector-coupled energy model (UCB-SEnMod) was developed. Its unique features are the modular structure, high flexibility, and applicability, enabling it to model any system constellation and can be easily extended with new functions due to its software design. Due to the software architecture, it is possible to map individual buildings or companies and regions, or even countries. In addition, we plan to make the energy model UCB-SEnMod available as an open-source framework to enable users to understand the functionality and configuration options more easily. This paper presents the methodology of the UCB-SEnMod model. The main components of the model are described in detail, i.e., the energy generation systems, the consumption components in the electricity, heat, and transport sectors, and the possibilities of load balancing.
This paper presents a feasibility study for the production of recycled glycol modified polyethylene terephthalate (PETG) material for additive manufacturing. Past studies showed a variety of results for the recycling of 3D-printing material, therefore the precise effect on the material properties is not completely clear. For this work, PETG waste of the same grade was recycled once and further processed into 3D printing filament. The study compares three blend ratios between purchased plastic pellets and recycled pellets to determine the degradation effect of one recycling cycle and possible blend ratios to counter these effects. Furthermore, the results include a commercially available filament. The comparison uses the filament diameter, the dimensional accuracy of the printed test specimen and mechanical properties as quality criteria. The study shows that the recycled material has a minor decrease concerning the tensile strength and Young’s modulus.
Carbon footprinting of universities worldwide: Part I — objective comparison by standardized metrics
(2021)
Background: Universities, as innovation drivers in science and technology worldwide, should be leading the Great Transformation towards a carbon–neutral society and many have indeed picked up the challenge. However, only a small number of universities worldwide are collecting and publishing their carbon footprints, and some of them have defined zero emission targets. Unfortunately, there is limited consistency between the reported carbon footprints (CFs) because of different analysis methods, different impact measures, and different target definitions by the respective universities.
Results: Comprehensive CF data of 20 universities from around the globe were collected and analysed. Essential factors contributing to the university CF were identified. For the first time, CF data from universities were not only compared. The CF data were also evaluated, partly corrected, and augmented by missing contributions, to improve the consistency and comparability. The CF performance of each university in the respective year is thus homogenized, and measured by means of two metrics: CO2e emissions per capita and per m2 of constructed area. Both metrics vary by one order of magnitude across the different universities in this study. However, we identified ten universities reaching a per capita carbon footprint of lower than or close to 1.0 Mt (metric tons) CO2e/person and year (normalized by the number of people associated with the university), independent from the university’s size. In addition to the aforementioned two metrics, we suggested a new metric expressing the economic efficiency in terms of the CF per $ expenditures and year. We next aggregated the results for all three impact measures, arriving at an overall carbon performance for the respective universities, which we found to be independent of geographical latitude. Instead the per capita measure correlates with the national per capita CFs, and it reaches on average 23% of the national impacts per capita. The three top performing universities are located in Switzerland, Chile, and Germany.
Conclusion: The usual reporting of CO2 emissions is categorized into Scopes 1–3 following the GHG Protocol Corporate Accounting Standard which makes comparison across universities challenging. In this study, we attempted to standardize the CF metrics, allowing us to objectively compare the CF at several universities. From this study, we observed that, almost 30 years after the Earth Summit in Rio de Janeiro (1992), the results are still limited. Only one zero emission university was identified, and hence, the transformation should speed up globally.
Background: As electric kick scooters, three-wheelers, and passenger cars enter the streets, efficiency trade-offs across vehicle types gain practical relevance for consumers and policy makers. Here, we compile a comprehensive dataset of 428 electric vehicles, including seven vehicle types and information on certified and real-world energy consumption. Regression analysis is applied to quantify trade-offs between energy consumption and other vehicle attributes.
Results: Certified and real-world energy consumption of electric vehicles increase by 60% and 40%, respectively, with each doubling of vehicle mass, but only by 5% with each doubling of rated motor power. These findings hold roughly also for passenger cars whose energy consumption tends to increase 0.6 ± 0.1 kWh/100 km with each 100 kg of vehicle mass. Battery capacity and vehicle mass are closely related. A 10 kWh increase in battery capacity increases the mass of electric cars by 15 kg, their drive range by 40–50 km, and their energy consumption by 0.7–1.0 kWh/100 km. Mass-produced state-of-the-art electric passenger cars are 2.1 ± 0.8 kWh/100 km more efficient than first-generation vehicles, produced at small scale.
Conclusion: Efficiency trade-offs in electric vehicles differ from those in conventional cars—the latter showing a strong dependency of fuel consumption on rated engine power. Mass-related efficiency trade-offs in electric vehicles are large and could be tapped by stimulating mode shift from passenger cars to light electric road vehicles. Electric passenger cars still offer potentials for further efficiency improvements. These could be exploited through a dedicated energy label with battery capacity as utility parameter.
Since operational managers often monitor large numbers of wind turbines (WTs), they depend on a toolset to provide them with highly condensed information to identify and prioritize low performing WTs or schedule preventive maintenance measures. Power curves are a frequently used tool to assess the performance of WTs. The power curve health value (HV) used in this work is supposed to detect power curve anomalies since small deviations in the power curve are not easy to identify. It evaluates deviations in the linear region of power curves by performing a principal component analysis. To calculate the HV, the standard deviation in direction of the second principal component of a reference data set is compared to the standard deviation of a combined data set consisting of the reference data and data of the evaluated period. This article examines the applicability of this HV for different purposes as well as its sensitivities and provides a modified HV approach to make it more robust and suitable for heterogeneous data sets. The modified HV was tested based on ENGIE's open data wind farm and data of on- and offshore WTs from the WInD-Pool. It proved to detect anomalies in the linear region of the power curve in a reliable and sensitive manner and was also eligible to detect long term power curve degradation. Also, about 7 % of all corrective maintenance measures were preceded by high HVs with a median alarm horizon of three days. Overall, the HV proved to be a promising tool for various applications.