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Optimal integration of sustainable multi-energy systems in districts
No full textThe integration of renewable energy sources within the built environment is a compelling challenge to tackle. With the recent climate targets aiming at a carbon neutral energy supply by the mid-century, a modern building energy system (BES) should maximize the use of sustainable energy resources while coping with the technical constraints of the network infrastructure in balancing seasonal demand and intermittent generation. In order to overcome the latter limitations, traditional BES sizing and scheduling methods need to shift from a building-oriented towards a district-oriented approach to fully benefit from local resources as well as from potential energy service synergies between different types of final users. In view of the current context, this paper proposes a novel method for the integration of sustainable multi-energy systems in different district types of Switzerland. Based on a robust MILP modelling framework, the method consists of two distinct steps: in the first phase, the optimal BES sizing and scheduling problem is solved systematically, generating a set of different BES options for each building type located within the studied district. In the second phase, the final BES configuration is selected among the generated options considering potential synergies within the district. At this stage, local resources such as woody and wet biomass, multi-energy technologies such as power-to-gas and seasonal storage, as well as heating and cooling networks are included in the problem formulation. The proposed method is validated on hands of different case studies: a rural, suburban and urban district configuration. Preliminary results prove that the implementation of a district-oriented method allows for a significant decrease in system costs in comparison to a building-oriented approach while granting equivalent environmental performances. Moreover, the results highlight the potential of district scale multi-energy systems in reaching climatic targets as well as the future role of network operators.SCI-STI-F
In vitro measurement of bone-implant micromotion of tibial implants after total ankle replacement
No full textBackground: Total Ankle Replacement (TAR) o_ers the advantage of preserving ankle joint motion compared with ankle arthrodesis. However, compared to hip or knee arthroplasty, TAR is a less successful procedure when considering the relatively low implants survival rate at 10 years, the high revision rate and also numerous complications. The main complications after TAR are periprosthetic cysts (up to 77% of cases), fractures of the malleolus and bone loosening. However, the mechanism that leads to these complications remains unclear and the high incidence of periprosthetic cysts raises worries for long-term survivorship of the implants. Therefore, there is a need for better understanding of shortcomings associated with TAR, and for improvement in implant design and surgical technique. Goal: To determine, in vitro, the in_uence of mobile-bearing position on bone strain and primary stability after Total Ankle Replacement by measuring bone surface strain and bone-implant relative movement. The study was focused on tibial components. Methods: Prostheses from the company Tornier were implanted in 8 cadaveric tibiae by an experienced surgeon. A load of 2000 [N] was applied on the prosthesis at three di_erent positions: posterior, neutral and anterior. The displacement was measured on the anterior surface of the tibia using 3D Digital Image Correlation. The bone axial strain was then evaluated from the displacement. Results: Bone strain was higher when the mobile-bearing was placed in the anterior position compared with the neutral position (p 4000 microstrain). The bone-implant relative movements were in the same order of magnitude of in vivo measurements reported in the literature. In conclusion, we confirmed the importance of mobile-bearing position in TAR, which could be optimized during surgery.LB
A Machine Learning-based Framework for Forecasting Sales of New Products With Short Life Cycles Using Deep Neural Networks
No full textDemand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX’s performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods.TO
Frequent concomitant presence of Desulfitobacterium spp. and “Dehalococcoides” spp. in chloroethene-dechlorinating microbial communities
No full textThe presence of chloroethene dechlorination activity as well as several bacterial genera containing mainly organohalide respiring members was investigated in 34 environmental samples from 18 different sites. Cultures inoculated with these environmental samples on tetrachloroethene and amended weekly with a seven organic electron donor mixture resulted in eleven enrichments with cis-DCE, ten with VC, and eleven with ethene as dechlorination end product, and only two where no dechlorination was observed. “Dehalococcoides” spp. and Desulfitobacterium spp. were detected in the majority of the environmental samples independently of the dechlorination end product formed. The concomitant presence of “Dehalococcoides” spp. and Desulfitobacterium spp. in the majority of the enrichments suggested that chloroethene dechlorination was probably the result of catalysis by at least two organohalide respiring genera either in parallel or by stepwise catalysis. A more detailed study of one enrichment on cis-DCE suggested that in this culture Desulfitobacterium spp. as well as “Dehalococcoides” spp. dechlorinated cis-DCE whereas dechlorination of VC was only catalyzed by the latter.LBENational Licence
Watertable fluctuations in coastal unconfined aquifers with a sloping sea boundary: Vertical flow and dynamic effective porosity effects
No full textInteractions between the tide and sloping sea boundary make watertable fluctuations in coastal unconfined aquifers complicated. Based on a perturbation method, we derived a new analytical solution to predict watertable fluctuations for coastal unconfined aquifers with a sloping sea boundary. Following validation with a numerical model, the analytical solution was used to explore the effects of the vertical flow (in the saturated zone) and dynamic effective porosity on watertable fluctuations. Results show that the new analytical solution accurately predicts watertable fluctuations for coastal unconfined aquifers with a sloping sea boundary. Compared with sand coastal unconfined aquifers, both vertical flow and dynamic effective porosity effects on watertable fluctuations are more pronounced for loam coastal unconfined aquifers. Vertical flow has a minor influence on the fluctuation amplitude while it significantly decreases the phase lag of the watertable fluctuation at a given location. In contrast to vertical flow, accounting for the dynamic effective porosity not only decreases the phase lag, but also significantly amplifies the fluctuation amplitude for a given location, which enables watertable wave propagation further inland. Increasing the beach slope weakens the effects of the vertical flow and dynamic effective porosity on watertable fluctuations. Furthermore, including either the vertical flow or dynamic effective porosity effects leads to a lower watertable overheight. These results highlight the importance of vertical flow and dynamic effective porosity effects in models of watertable fluctuations.ECO
Power Smoothing and High-Power Fast Energy Exchange Between Storage Systems
No full textSupercapacitors as new energy storage components - Generalities - Supercapacitors used as complementary energy storage devices - Supercapacitors as main energy storage devices Powering a bus with supercapacitors - Supercapacitrs as a main energy source - Constraints on a power supply Sequential distribution with double storage : "le biberonnage" - Minimisting the constraints on a power supply - Constraints on a power supply - Fast energy exchangeLE
Scalable semantic 3D mapping of coral reefs with deep learning
No full textCoral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular structure-from-motion (SfM) photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability. This paper presents a new paradigm for mapping underwater environments from ego-motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images. The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high-precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: given a trained model, a 100 m video transect acquired within 5 min of diving with a cheap consumer-grade camera can be fully automatically transformed into a semantic point cloud within 5 min. We demonstrate the spatial accuracy of our method and the semantic segmentation performance (of at least 80% total accuracy), and publish a large dataset of ego-motion videos from the northern Gulf of Aqaba, along with a dataset of video frames annotated for dense semantic segmentation of benthic classes. Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method advances coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning-based Structure-from-Motion has broad implications for fast low-cost mapping of underwater environments other than coral reefs.ECEOLG
Near ambient-pressure X-ray photoelectron spectroscopy study of CO2 activation and hydrogenation on indium/copper surface
No full textIndium-based catalysts exhibit excellent performance for CO2 hydrogenation to methanol, yet their nature and chemical evolution under reaction conditions are still elusive, thus hindering an understanding of their reaction mechanism. In this work, near ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS) is employed to investigate the chemical properties and the catalytic role of indium/copper model catalysts under CO2 hydrogenation conditions. We found that the deposition of In on the surface of a Cu foil led to the formation of Cu-In alloy, whereas upon CO2 exposure, In was partially oxidized to In2O3-x and Cu remains metallic. Due to the presence of In2O3-x, CO2 was activated on the surface of In/Cu samples mainly in the form of carbonate. In addition, compared with the pure In foil reference, both the fraction of oxygen vacancies and the coverage density of carbonate were higher on the In/Cu samples, indicating the promotion effect of Cu-In alloy in the activation of CO2. These results reveal the evolution of the active sites of indium/copper catalysts and inspire the design of advanced In-based bimetallic catalysts for CO2 hydrogenation.LMERThis is an Open Access article under the terms of the Creative Commons Attribution Licens
MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition
No full textThe numerical solution of partial differential equations (PDEs) depending on para- metrized or random input data is computationally intensive. Reduced order modeling techniques, such as the reduced basis methods, have been developed to alleviate this computational burden, and are nowadays exploited to accelerate real-time analysis, as well as the solution of PDE-constrained optimization and inverse problems. These methods are built upon low-dimensional spaces obtained by selecting a set of snap- shots from a parametrically induced manifold. However, for these techniques to be effective, both parameter-dependent and random input data must be expressed in a convenient form. To address the former case, the empirical interpolation method has been developed. In the latter case, a spectral approximation of stochastic fields is often generated by means of a Karhunen-Loève expansion. In all these cases, a low dimensional space to represent the function being approximated (PDE solution, parametrized data, stochastic field) can be obtained through proper orthogonal de- composition. Here, we review possible ways to exploit this methodology in these three contexts, we recall its optimality properties, and highlight the common mathematical structure beneath.CMCSMATHICSE Technical Report Nr. 01.2016 January 2016 (New 25.05.2016
Enhancing Algae Biomass Production by Using Dye-Sensitized Solar Cells as Filters
No full textOne of the most promising options for decreasing the costs of microalgae production is enhancing the production and reducing the energy demand of the culturing systems and the high surface area requirements. Because microalgae growth requires only specific wavelengths of the solar spectrum, the remaining part of the solar spectrum may be simultaneously used by a translucent photovoltaic (PV) layer to produce electricity, which leads to a reduction of space and energy requirements. This work presents the results of a new concept of a positive energy culturing system for microalgae, where the light source is selectively shared between the needs of the algal biomass through photosynthesis and the production of PV energy through dye-sensitized solar cells (DSCs). To ascertain the DSC (DSC-Red, DSC-Green) light-filtering effects on microalgal biomass, (1) the variation of growth kinetics, (2) microalgae pigments [chlorophylls-(a + b) and carotenoids], and (3) macromolecule content (carbohydrates, proteins, and lipids) were investigated and compared to control cultures under two different solar-simulated light intensities (200 and 600 W/m2). The results showed a net improvement of the growth rate and dry weight at the higher irradiance using both colored DSC filters compared to control cultures. The highest growth rates (μ) and doubling time (td) of Chlorella vulgaris cells were obtained using the DSC-Red (DSC-R) and DSC-Green (DSC-G) solar cells as filters with μ = 0.86 ± 0.01 day–1; td = 0.80 day and μ = 0.85 ± 0.03 day–1; td = 0.81 day, respectively, compared to normal glass control μ = 0.51 ± 0.03 day–1; td = 1.35 day. A significant increase in the chlorophyll-a content was obtained under low light intensity for both DSC-colored compared to control culture, and there was no significant variation in the macromolecule content measured under the tested light intensities. Finally, a life cycle assessment based on a functional unit of 1 kg of the produced algal biomass using the DSC–photobioreactor (DSC–PBR) was performed and compared to a normal glass PBR. The results were expressed in terms of CO2 emission equivalents produced and electricity generated. A fraction of electricity generated by DSC–PBR is used for bubbling, and the extra electricity is injected into the electricity grid. This resulted in net negative GHG emissions.GR-LUDGMFSCI-STI-F