Dosimetric effect by shallow air cavities in high energy electron beams

This study evaluates the dosimetric impact caused by an air cavity located at 2 mm depth from the top surface in a PMMA phantom irradiated by electron beams produced by a Siemens Primus linear accelerator. A systematic evaluation of the effect related to the cavity area and thickness as well as to the electron beam energy was performed by using Monte Carlo simulations (EGSnrc code), Pencil Beam algorithm and Gafchromic EBT2 films. A home-PMMA phantom with the same geometry as the simulated one was specifically constructed for the measurements. Our results indicate that the presence of the cavity causes an increase (up to 70%) of the dose maximum value as well as a shift forward of the position of the depthedose curve, compared to the homogeneous one. Pronounced dose discontinuities in the regions close to the lateral cavity edges are observed. The shape and magnitude of these discontinuities change with the dimension of the cavity. It is also found that the cavity effect is more pronounced (6%) for the 12 MeV electron beam and the presence of cavities with large thickness and small area introduces more significant variations (up to 70%) on the depthedose curves. Overall, the Gafchromic EBT2 film measurements were found in agreement within 3% with Monte Carlo calculations and predict well the fine details of the dosimetric change near the cavity interface. The Pencil Beam calculations underestimate the dose up to 40% compared to Monte Carlo simulations; in particular for the largest cavity thickness (2.8 cm).

A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Trends in properties of para-substituted 3-(phenylhydrazo)pentane-2,4-diones

Trends between the Hammett's sigma(p) and related normal sigma(n)(p), inductive sigma(I), resonance sigma(R), negative sigma(-)(p) and positive sigma(+)(p) polar conjugation and Taft's sigma(o)(p) substituent constants and the N-H center dot center dot center dot O distance, delta(N-H) NMR chemical shift, oxidation potential (E-p/2(ox), measured in this study by cyclic voltammetry (CV)) and thermodynamic parameters (pK, Delta G(0), Delta H-0 and Delta S-0) of the dissociation process of unsubstituted 3-(phenylhydrazo)pentane-2,4-dione (HL1) and its para-substituted chloro (HL2), carboxy (HL3), fluoro (HL4) and nitro (HL5) derivatives were recognized. The best fits were found for sigma(p) and/or sigma(-)(p) in the cases of d(N center dot center dot center dot O), delta(N-H) and E-p/2(ox), showing the importance of resonance and conjugation effects in such properties, whereas for the above thermodynamic properties the inductive effects (sigma(I)) are dominant. HL2 exists in the hydrazo form in DMSO solution and in the solid state and contains an intramolecular H-bond with the N center dot center dot center dot O distance of 2.588(3)angstrom. It was also established that the dissociation process of HL1-5 is non-spontaneous, endothermic and entropically unfavourable, and that the increase in the inductive effect (sigma(I)) of para-substitutents (-H < -Cl < -COOH < -F < -NO2) leads to the corresponding growth of the N center dot center dot center dot O distance and decrease of the pK and of the changes of Gibbs free energy, of enthalpy and of entropy for the HL1-5 acid dissociation process. The electrochemical behaviour of HL1-5 was interpreted using theoretical calculations at the DFT/HF hybrid level, namely in terms of HOMO and LUMO compositions, and of reactivities induced by anodic and cathodic electron-transfers. Copyright (C) 2010 John Wiley & Sons, Ltd.

Electric vehicles as a mean to reduce, energy, emissions and electricity costs

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), which obtain their fuel from the grid by charging a battery, are set to be introduced into the mass market and expected to contribute to oil consumption reduction. This research is concerned with studying the potential impacts on the electric utilities of large-scale adoption of plug-in electric vehicles from the perspective of electricity demand, fossil fuels use, CO2 emissions and energy costs. Simulations were applied to the Portuguese case study in order to analyze what would be the optimal recharge profile and EV penetration in an energy-oriented, an emissions-oriented and a cost-oriented objective. The objectives considered were: The leveling of load profiles, minimization of daily emissions and minimization of daily wholesale costs. Almost all solutions point to an off-peak recharge and a 50% reduction in daily wholesale costs can be verified from a peak recharge scenario to an off-peak recharge for a 2 million EVs in 2020. A 15% improvement in the daily total wholesale costs can be verified in the costs minimization objective when compared with the off-peak scenario result.

alpha-Co(OH)(2)/carbon nanofoam composite as electrochemical capacitor electrode operating at 2 V in aqueous medium

In this work, alpha-Co(OH)(2) is electrodeposited onto carbon nanofoam forming a composite electrode operating in a potential window of 2 V in aqueous medium. Prior to electrodeposition, the carbon nanofoam substrate is subjected to a functionalization process, which leads to an increase of about 40% in its specific capacitance value. Formation of cobalt hydroxide clusters onto the functionalized carbon nanofoam by pulse electrodeposition further enhances the specific capacitance of the electrode. The combination of these factors with an enlarged working potential window, results in a material with specific capacitance close to 300 F g(-1) at current density of 1 A g(-1), considering the total mass loading of the composite. This suggests the potential application of the prepared composites in high energy density electrochemical supercapacitors. (c) 2015 Elsevier B.V. All rights reserved.