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L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

We discontinued this version of the dataset.

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

The purpose of this study was to investigate the influence of increasing sulfate concentrations on chromium removal, to evaluate the effect of the presence of Cr(VI) on sulfate removal by Streptomyces sp. MC1 and to analyze the differential protein expression profile in the presence of this metal for the identification of proteins repressed or overexpressed. In the presence of Cr(VI) but in the absence of sulfate ions, bacterial growth was negligible, showing the Cr(VI) toxicity for this bacterium. However, the sulfate presence stimulated bacterium growth and Cr(VI) removal, regardless of its concentrations. Streptomyces sp. MC1 showed ability to remove chromium and sulfate simultaneously. Also, the sulfate presence favored the decrease of total chromium concentration from supernatants reaching a decrease of 50% at 48 h. In presence of chromium, seven proteins were down‐expressed and showed homology to proteins involved in protein biosynthesis, energy production and free radicals detoxification while two proteins involved in oxidation‐reduction processes identified as dihydrolipoamide dehydrogenase and S‐adenosyl‐l‐methionine synthase were overexpressed.

Abstract This study sought to quantify and characterize cassava waste as fuel. The wastes from three cultivars were collected to study and were divided into three distinct parts of the cassava plant: seed stem, thick stalks, and thin stalks. Physical and chemical analyzes were carried out to determine the elemental composition of the waste: volatile matter; fixed carbon; ash; moisture; lignin; cellulose; hemicellulose; ash composition and higher heating value were determined. We conducted a thermogravimetric analysis in oxidizing and inert atmospheres to study the behavior of the waste as fuel. The root productivity obtained ranged from 7.7 to 13.0 t ha−1 yr−1 on a dry basis (db), and the ratio between waste and roots varied from 0.36 to 0.91. The physical and chemical properties of cassava waste are analogous to those of woody biomass regarding the elemental composition, the higher heating value, and thermogravimetric analysis. Ash content varied from 2.5% to 3.5%, reaching around 6.0% in samples unwashed. Approximately 60% of the ashes are alkali oxides, especially P2O5, K2O, and CaO, which have low melting points. The alkali index calculated suggests that there is a strong tendency that the combustion process leads to ash fouling and the formation of ash deposits.

This paper estimates the profit of the joint operation of a wind farm and a li-ion battery energy storage system (BESS) in the Iberian electricity market (MIBEL). The day-ahead and first intraday energy markets, and the tertiary regulation market are considered to optimize the joint operation of both assets. A rolling window combined with a non-linear optimization model are used to design the operation strategy. The BESS lifetime (as a function of the depth of discharge) is considered in the optimization problem, and different BESS capacities and initial state of charge values are tested to determine their approximate optimum values. The model and strategy designed were tested using real data from the MIBEL market and predicted data from Sotavento wind farm. The resulting incomes were compared to the BESS investments costs to determine, for a given capacity, when the project becomes viable.

This study evaluated the environmental conditions of two watersheds selected on the basis of similarities in land use. The environmental conditions of the watersheds were analyzed using two geoprocessing methods and in situ evaluations by applying the rapid assessment protocol (RAP). Geospatial image processing was used to analyze land use, slope, soil classification, and rainfall, while RAP was used to evaluate the connectivity and size of riparian corridors. The results revealed varied uses of land with the landscape matrix in both watersheds, composed of agriculture, pasture, and urban centers. Urban centers were defined as spots and riparian zones as corridors. The analysis of environmental fragility considering all the geospatial variables, classified both watersheds as having medium fragility. The most fragile sites were the urban centers, which had a high slope and degraded riparian zone. Onsite assessments have shown that corridors do not have the size required by legislation; they are fragmented and unstructured, and they contain exotic species. We also propose that a multi-factorial approach be used to evaluate watersheds, associating geospatial assessments and onsite analyses that consider the limitations pointed out in each protocol. This reduces sampling errors and shows the actual state of conservation in riparian zones.

In this study, pyrolysis of residual biomass from the agro-alimentary industry of Argentina was investigated. The studied biomasses were rice husk, peanut shell and wheat straw. The effect of pyrolysis temperature over solid (bio-char), liquid (bio-oil) and gas (bio-gas) fractions weight yields were evaluated for each biomass, in the range of 350-650 °C. The maximum yield of bio-oil was obtained at 550 °C for rice husk (45 wt%) and wheat straw (58 wt%), and at 500 °C for peanut shells (51 wt%). At those temperatures, the data recorded the smallest spread around the mean. Different characterization techniques of raw biomass were reported, namely, thermal behavior; proximate and ultimate analysis; content of Ca, Al, K, Si and Fe; and hemicellulose, cellulose and lignin composition. Bio-oils characterization included water content and chemical compounds identification. Furthermore, bio-char HHV and BET surface area were measured. Rice husk bio-oil showed 21% selectivity towards furans of which 75% corresponded to furfural and 5-HMF. Peanut shell bio-char presented the highest Higher Heating Value (7250 kcal/kg) and BET surface area (215 m2/g). In addition, co-pyrolysis reactions and the synergistic effects over obtained products completed this study. Co-pyrolysis bio-oils yields varied between 41 and 46 wt%, for all the mixtures. Bio-oil water content decreased up to 15% for rice husk mixed with peanut shell or wheat straw. Moreover, 5-HMF was detected in all bio-oils, and furfural selectivity was higher than 5% in the three mixtures investigated.