• Energy Research
• Restricted close
• ES close
• EU close
• UA close

AbstractExtreme weather events can have strong negative impacts on species survival and community structure when surpassing lethal thresholds. Extreme, short‐lived, winter warming events in the Arctic rapidly melt snow and expose ecosystems to unseasonably warm air (for instance, 2–10 °C for 2–14 days) but upon return to normal winter climate exposes the ecosystem to much colder temperatures due to the loss of insulating snow. Single events have been shown to reduce plant reproduction and increase shoot mortality, but impacts of multiple events are little understood as are the broader impacts on community structure, growth, carbon balance, and nutrient cycling. To address these issues, we simulated week‐long extreme winter warming events – using infrared heating lamps and soil warming cables – for 3 consecutive years in a sub‐Arctic heathland dominated by the dwarf shrubsEmpetrum hermaphroditum, Vaccinium vitis‐idaea(both evergreen) andVaccinium myrtillus(deciduous). During the growing seasons after the second and third winter event, spring bud burst was delayed by up to a week forE. hermaphroditumandV. myrtillus, and berry production reduced by 11–75% and 52–95% forE. hermaphroditumandV. myrtillus, respectively. Greater shoot mortality occurred inE. hermaphroditum(up to 52%),V. vitis‐idaea(51%), andV. myrtillus(80%). Root growth was reduced by more than 25% but soil nutrient availability remained unaffected. Gross primary productivity was reduced by more than 50% in the summer following the third simulation. Overall, the extent of damage was considerable, and critically plant responses were opposite in direction to the increased growth seen in long‐term summer warming simulations and the ‘greening’ seen for some arctic regions. Given the Arctic is warming more in winter than summer, and extreme events are predicted to become more frequent, this generates large uncertainty in our current understanding of arctic ecosystem responses to climate change.

Artículos en revistas When facing any forecasting problem not only is accuracy on the predictions sought. Also, useful information about the underlying physics of the process and about the relevance of the forecasting variables is very much appreciated. In this paper, it is presented an automatic specification procedure for models that are based on additivity assumptions and piecewise linear regression. This procedure allows the analyst to gain insight about the problem by examining the automatically selected model, thus easily checking the validity of the forecast. Monte Carlo simulations have been run to ensure that the model selection procedure behaves correctly under weakly dependent data. Moreover, comparison over other well-known methodologies has been done to evaluate its accuracy performance, both in simulated data and in the context of short-term natural gas demand forecasting. Empirical results show that the accuracy of the proposed model is competitive against more complex methods such as neural networks. info:eu-repo/semantics/publishedVersion

Abstract Simulation and optimization techniques are the pillars of for the Virtual Commissioning of modern digital factories. In particular, industrial production lines are complex systems formed by a set of machines that have several cross-dependencies (i. e., the production efficiency of a machine impact deeply performance figures of the others and affects the whole line productivity). The simulation and optimization of production lines is an integrated approach that allows finding efficiently optimized production parameters. This paper presents an example of application of the simulation to the virtual design and optimization of an industrial production line.

Artículos en revistas To achieve an effective integration of the electric vehicle in the grid, not only technical issues must be solved, but also a new regulatory context and business models must be proposed. This paper proposes two electric vehicle integration business models; one resembles the mobile-phone business, while the other is more based on current practices in the electricity-supply business. Decomposition into three layers: physical, management, and business, is used to describe those business models. Furthermore, electric vehicle charging scenarios are presented to illustrate how the proposed business models fit in each of the potential situations. Finally, several practical study cases are presented. info:eu-repo/semantics/publishedVersion

Artículos en revistas South America is one of the most dynamic regions for the joint development of natural gas and electricity. Gas is abundant in the region, although unevenly distributed (see Figure 1), with significant new reserves projected in Brazil (in its Pre-Salt basins) and in Argentina (shale gas). Unconventional gas reserves in South America are estimated at 1,430 trillion ft3. info:eu-repo/semantics/publishedVersion

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

A mathematical model was developed to simulate solar drying of grapes, integrating heat and mass transfer models solved by an explicit finite differences method, considering changing boundary conditions. The model simultaneously incorporated shrinkage of the product, changes in effective moisture diffusivity and dependence of thermal properties on water content and temperature. Field experiments were carried out in a mixed mode solar dryer located in the North of Portugal, with pre-blanched grapes. A good prediction of experimental solar drying curves was attained. The mathematical model can be applied for simulating solar drying of different foods, once known their specific thermo-physical properties. Simulations obtained with the developed model can be valuable for predicting accurate drying times and consequently to design, control and optimise the production of dried foods.

This study aimed to evaluate the potential of microalgae grown in wastewater to recover carotenoids and biogas in a biorefinery approach. To this end, two pilot raceway ponds treating real wastewater were operated with a hydraulic retention time (HRT) of 8 and 6 days. The maximum total carotenoid content in harvested biomass was 4.3 mg·gDW-1 with a HRT of 8 days, while it stabilized at 1.5–2.2 mg·gDW-1 with a HRT of 6 days. Biochemical methane potential tests were then run to quantify the biogas production from carotenoid-extracted biomass (136.3 mLCH4·gVS-1), which corresponded to 86% of the methane yield achievable from raw microalgal biomass. Co-digestion with primary sludge increased the methane yield production both for raw and carotenoid-extracted biomass by 58 and 86%, respectively, when a 75:25 ratio was employed and by 28 and 44%, respectively, when a 50:50 ratio (VS basis) was used. This approach is promising for recovering resources (bio-based products and bioenergy) in the framework of a circular bioeconomy. This research has received funding from MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe’’ under the projects AL4BIO (RTI2018-099495-B-C21) and Cyan2Bio (PID2021-126564OB-C32). E. Ruales acknowledges her grant 2020 FISDUR 00592 funded by AGAUR (Generalitat de Catalunya). M. Bellver acknowledges her grant PRE2019-091552 funded by MCIN/AEI/ 10.13039/501100011033 and “ESF Investing in your future”.