999 resultados para Resource Extraction


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A discussion of the most interesting results obtained in our laboratories, during the supercritical CO(2) extraction of bioactive compounds from microalgae and volatile oils from aromatic plants, was carried out. Concerning the microalgae, the studies on Botryococcus braunii and Chlorella vulgaris were selected. Hydrocarbons from the first microalgae, which are mainly linear alkadienes (C(23)-C(31)) with an odd number of carbon atoms, were selectively extracted at 313 K increasing the pressure up to 30.0 MPa. These hydrocarbons are easily extracted at this pressure, since they are located outside the cellular walls. The extraction of carotenoids, mainly canthaxanthin and astaxanthin, from C. vulgaris is more difficult. The extraction yield of these components at 313 K and 35.0 MPa increased with the degree of crushing of the microalga, since they are not extracellular. On the other hand, for the extraction of volatile oils from aromatic plants, studies on Mentha pulegium and Satureja montana L were chosen. For the first aromatic plant, the composition of the volatile and essential oils was similar, the main components being the pulegone and menthone. However, this volatile oil contained small amounts of waxes, which content decreased with decreasing particle size of the plant matrix. For S. montana L it was also observed that both oils have a similar composition, the main components being carvacrol and thymol. The main difference is the relative amount of thymoquinone, which content can be 15 times higher in volatile oil. This oxygenated monoterpene has important biological activities. Moreover, experimental studies on anticholinesterase activity of supercritical extracts of S. montana were also carried out. The supercritical nonvolatile fraction, which presented the highest content of the protocatechuic, vanilic, chlorogenic and (+)-catechin acids, is the most promising inhibitor of the enzyme butyrylcholinesterase. In contrast, the Soxhlet acetone extract did not affect the activity of this enzyme at the concentrations tested. (C) 2011 Elsevier B.V. All rights reserved.

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This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

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This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

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Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

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Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

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The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

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Energy resources management can play a very relevant role in future power systems in a SmartGrid context, with intensive penetration of distributed generation and storage systems. This paper deals with the importance of resource management in incident situations. The paper presents DemSi, an energy resources management simulator that has been developed by the authors to simulate electrical distribution networks with high distributed generation penetration, storage in network points and customers with demand response contracts. DemSi is used to undertake simulations for an incident scenario, evidencing the advantages of adequately using flexible contracts, storage, and reserve in order to limit incident consequences.

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Jornadas "Ciência nos Açores – que futuro? Tema Ciências Naturais e Ambiente", Ponta Delgada, 7-8 de Junho de 2013.

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10th International Temperate Reefs Symposium, The University of Western Australia, 12-17 de janeiro.

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In this work, a microwave-assisted extraction (MAE) methodology was compared with several conventional extraction methods (Soxhlet, Bligh & Dyer, modified Bligh & Dyer, Folch, modified Folch, Hara & Radin, Roese-Gottlieb) for quantification of total lipid content of three fish species: horse mackerel (Trachurus trachurus), chub mackerel (Scomber japonicus), and sardine (Sardina pilchardus). The influence of species, extraction method and frozen storage time (varying from fresh to 9 months of freezing) on total lipid content was analysed in detail. The efficiencies of methods MAE, Bligh & Dyer, Folch, modified Folch and Hara & Radin were the highest and although they were not statistically different, differences existed in terms of variability, with MAE showing the highest repeatability (CV = 0.034). Roese-Gottlieb, Soxhlet, and modified Bligh & Dyer methods were very poor in terms of efficiency as well as repeatability (CV between 0.13 and 0.18).

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This paper reports a novel application of microwave-assisted extraction (MAE) of polyphenols from brewer’s spent grains (BSG). A 24 orthogonal composite design was used to obtain the optimal conditions of MAE. The influence of the MAE operational parameters (extraction time, temperature, solvent volume and stirring speed) on the extraction yield of ferulic acid was investigated through response surface methodology. The results showed that the optimal conditions were 15 min extraction time, 100 °C extraction temperature, 20 mL of solvent, and maximum stirring speed. Under these conditions, the yield of ferulic acid was 1.31±0.04% (w/w), which was fivefold higher than that obtained with conventional solid–liquid extraction techniques. The developed new extraction method considerably reduces extraction time, energy and solvent consumption, while generating fewer wastes. HPLC-DADMS analysis indicated that other hydroxycinnamic acids and several ferulic acid dehydrodimers, as well as one dehydrotrimer were also present, confirming that BSG is a valuable source of antioxidant compounds.

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This paper presents the study of the remediation of sandy soils containing six of the most common contaminants (benzene, toluene, ethylbenzene, xylene, trichloroethylene and perchloroethylene) using soil vapour extraction (SVE). The influence of soil water content on the process efficiency was evaluated considering the soil type and the contaminant. For artificially contaminated soils with negligible clay contents and natural organic matter it was concluded that: (i) all the remediation processes presented efficiencies above 92%; (ii) an increase of the soil water content led to a more time-consuming remediation; (iii) longer remediation periods were observed for contaminants with lower vapour pressures and lower water solubilities due to mass transfer limitations. Based on these results an easy and relatively fast procedure was developed for the prediction of the remediation times of real soils; 83% of the remediation times were predicted with relative deviations below 14%.

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Soil vapor extraction (SVE) is an efficient, well-known and widely applied soil remediation technology. However, under certain conditions it cannot achieve the defined cleanup goals, requiring further treatment, for example, through bioremediation (BR). The sequential application of these technologies is presented as a valid option but is not yet entirely studied. This work presents the study of the remediation of ethylbenzene (EB)-contaminated soils, with different soil water and natural organic matter (NOMC) contents, using sequential SVE and BR. The obtained results allow the conclusion that: (1) SVE was sufficient to reach the cleanup goals in 63% of the experiments (all the soils with NOMC below 4%), (2) higher NOMCs led to longer SVE remediation times, (3) BR showed to be a possible and cost-effective option when EB concentrations were lower than 335 mg kgsoil −1, and (4) concentrations of EB above 438 mg kgsoil −1 showed to be inhibitory for microbial activity.

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An accurate and sensitive method for determination of 18 polycyclic aromatic hydrocarbons (PAHs) (16 PAHs considered by USEPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) in fish samples was validated. Analysis was performed by microwave-assisted extraction and liquid chromatography with photodiode array and fluorescence detection. Response surface methodology was used to find the optimal extraction parameters. Validation of the overall methodology was performed by spiking assays at four levels and using SRM 2977. Quantification limits ranging from 0.15–27.16 ng/g wet weight were obtained. The established method was applied in edible tissues of three commonly consumed and commercially valuable fish species (sardine, chub mackerel and horse mackerel) originated from Atlantic Ocean. Variable levels of naphthalene (1.03–2.95 ng/g wet weight), fluorene (0.34–1.09 ng/g wet weight) and phenanthrene (0.34–3.54 ng/g wet weight) were detected in the analysed samples. None of the samples contained detectable amounts of benzo[a]pyrene, the marker used for evaluating the occurrence and carcinogenic effects of PAHs in food.

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As macroalgas marinhas têm uma grande diversidade de aplicações. Nos Açores, várias espécies têm sido usadas tradicionalmente para alimentação humana e para extração de agar, um ficocolóide aplicado na indústria alimentar e farmacêutica. As exigências no controlo da qualidade das matérias-primas e as práticas atuais de colheita de macroalgas marinhas selvagens na Europa exigem uma gestão eficaz deste recurso natural e, simultaneamente, tornam premente a necessidade de se implementarem métodos de produção de biomassa controlados, nomeadamente, práticas de cultivo. Apesar da importância reconhecida da exploração sustentável dos recursos marinhos existentes nos Açores, não existe qualquer informação sobre a viabilidade do cultivo de macroalgas marinhas no Arquipélago. O objetivo principal do presente projeto é avaliar o potencial de cultivo de espécies de macroalgas marinhas selecionadas, bem como identificar as melhores práticas de recolha desse recurso natural.