Re-entrant phase behaviour of network fluids: A patchy particle model with temperature-dependent valence

We study a model consisting of particles with dissimilar bonding sites ("patches"), which exhibits self-assembly into chains connected by Y-junctions, and investigate its phase behaviour by both simulations and theory. We show that, as the energy cost epsilon(j) of forming Y-junctions increases, the extent of the liquid-vapour coexistence region at lower temperatures and densities is reduced. The phase diagram thus acquires a characteristic "pinched" shape in which the liquid branch density decreases as the temperature is lowered. To our knowledge, this is the first model in which the predicted topological phase transition between a fluid composed of short chains and a fluid rich in Y-junctions is actually observed. Above a certain threshold for epsilon(j), condensation ceases to exist because the entropy gain of forming Y-junctions can no longer offset their energy cost. We also show that the properties of these phase diagrams can be understood in terms of a temperature-dependent effective valence of the patchy particles. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3605703]

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

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.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Structural, electrical and magnetic studies of Co:SnO2 and (Co,Mo):SnO2 films prepared by pulsed laser deposition

Here we report on the structural, optical, electrical and magnetic properties of Co-doped and (Co,Mo)-codoped SnO2 thin films deposited on r-cut sapphire substrates by pulsed laser deposition. Substrate temperature during deposition was kept at 500 degrees C. X-ray diffraction analysis showed that the undoped and doped films are crystalline with predominant orientation along the [1 0 1] direction regardless of the doping concentration and doping element. Optical studies revealed that the presence of Mo reverts the blue shift trend observed for the Co-doped films. For the Co and Mo doping concentrations studied, the incorporation of Mo did not contribute to increase the conductivity of the films or to enhance the ferromagnetic order of the Co-doped films. (C) 2012 Elsevier B.V. All rights reserved.

Computing the phase diagram of binary mixtures: A patchy particle case study

We investigate the phase behaviour of 2D mixtures of bi-functional and three-functional patchy particles and 3D mixtures of bi-functional and tetra-functional patchy particles by means of Monte Carlo simulations and Wertheim theory. We start by computing the critical points of the pure systems and then we investigate how the critical parameters change upon lowering the temperature. We extend the successive umbrella sampling method to mixtures to make it possible to extract information about the phase behaviour of the system at a fixed temperature for the whole range of densities and compositions of interest. (C) 2013 AIP Publishing LLC.

Metal deposition using Ionic liquids

There is an interest to create zinc/tin alloys to replace cadmium as a corrosion protective coating material. Existing aqueous electroplating systems for these alloys are commercially available but have several limitations. Dangerous and highly toxic complexing agents are uses e.g. cyanides. To overcome these problems, ionic liquids could provide a solution to obtain an alloy containing 20 to 30% of zinc. Ionic liquids (IL’s) often have wider electrochemical windows which allow the deposition of e.g. refractive metals that can not be deposited from aqueous solutions. In IL’s it is often not necessary to add complexing agents. The Zn/Sn alloy deposition from IL’s is therefore a promising application for the plating industry. Nevertheless, there are some issues with this alternative for aqueous systems. The degradation of the organic components, the control of the concentration of two metals and the risk of a two phase deposition instead of an alloy had to be overcome first. It is the main purpose of this thesis to obtain a Zn/Sn alloy with 20% zinc using IL’s as an electrolyte. First a separate study was performed on both the zinc and the tin deposition. Afterwards, an attempt to deposit a Zn/Sn alloy was made. An introduction to a study about the electrodeposition of refractive metals concludes this work. It initiated the research for oxygen-free IL’s to deposit molybdenum or tungsten. Several parameters (temperature, metal source and concentration, organic complexing agents,…) were optimized for both the zinc, tin and zinc/tin deposition. Experiments were performed both in a parallel plate cell and a Hull cell, so as to investigate the effect of current density as well. Ethaline200 was selected as electrolyte. As substrate, brass and iron were selected, while as anode a plate of the metal to deposit was chosen, tin for the alloy. The best efficiencies were always obtained on brass; however the iron substrate resulted in the best depositions. A concentration of 0.27M ZnCl2, 0.07M SnCl2 with 0.015M of K3-HEDTA as complexant resulted in a deposition containing the desired alloy with the amount of 20% zinc and 80% tin with good appearance. Refractory metals as molybdenum and tungsten cannot be electrodeposited from aqueous solutions without forming a co-deposition with Ni, Co or Fe. Here, IL’s could again provide a solution. A first requirement is the dissolution of a metal source. MoO3 could be suitable, however there are doubts about using oxides. Oxygen-free IL’s were sought for. A first attempt was the combination of ZnCl2 with chlormequat (CCC), which gave liquids below 150°C in molar ratios of 2 : 1 and 3 : 1. Unfortuna tely, MoO3 didn’t dissolve in these IL’s. Another route to design oxygen-free IL’s was the synthesis of quaternary ammonium salts. None of the methods used, proved viable as reaction time was long and resulted in very low yields. Therefore, no sufficient quantities were obtained to perform the possible electrochemical behavior of refractive metals.

Monitorization of alveolar deposited surface area of nanoparticles and ultrafine particles in different environments

Nanotechnology is an important emerging industry with a projected annual market of around one trillion dollars by 2015. It involves the control of atoms and molecules to create new materials with a variety of useful functions. Although there are advantages on the utilization of these nano-scale materials, questions related with its impact over the environment and human health must be addressed too, so that potential risks can be limited at early stages of development. At this time, occupational health risks associated with manufacturing and use of nanoparticles are not yet clearly understood. However, workers may be exposed to nanoparticles through inhalation at levels that can greatly exceed ambient concentrations. Current workplace exposure limits are based on particle mass, but this criteria could not be adequate in this case as nanoparticles are characterized by very large surface area, which has been pointed out as the distinctive characteristic that could even turn out an inert substance into another substance exhibiting very different interactions with biological fluids and cells. Therefore, it seems that, when assessing human exposure based on the mass concentration of particles, which is widely adopted for particles over 1 μm, would not work in this particular case. In fact, nanoparticles have far more surface area for the equivalent mass of larger particles, which increases the chance they may react with body tissues. Thus, it has been claimed that surface area should be used for nanoparticle exposure and dosing. As a result, assessing exposure based on the measurement of particle surface area is of increasing interest. It is well known that lung deposition is the most efficient way for airborne particles to enter the body and cause adverse health effects. If nanoparticles can deposit in the lung and remain there, have an active surface chemistry and interact with the body, then, there is potential for exposure. It was showed that surface area plays an important role in the toxicity of nanoparticles and this is the metric that best correlates with particle-induced adverse health effects. The potential for adverse health effects seems to be directly proportional to particle surface area. The objective of the study is to identify and validate methods and tools for measuring nanoparticles during production, manipulation and use of nanomaterials.

Particle agglomeration study in rf silane plasmas: In situ study by polarization-sensitive laser light scattering

To determine self-consistently the time evolution of particle size and their number density in situ multi-angle polarization-sensitive laser light scattering was used. Cross-polarization intensities (incident and scattered light intensities with opposite polarization) measured at 135 degrees and ex situ transmission electronic microscopy analysis demonstrate the existence of nonspherical agglomerates during the early phase of agglomeration. Later in the particle time development both techniques reveal spherical particles again. The presence of strong cross-polarization intensities is accompanied by low-frequency instabilities detected on the scattered light intensities and plasma emission. It is found that the particle radius and particle number density during the agglomeration phase can be well described by the Brownian free molecule coagulation model. Application of this neutral particle coagulation model is justified by calculation of the particle charge whereby it is shown that particles of a few tens of nanometer can be considered as neutral under our experimental conditions. The measured particle dispersion can be well described by a Brownian free molecule coagulation model including a log-normal particle size distribution. (C) 1996 American Institute of Physics.

Environmental impact caused by fungal and particle contamination of Portuguese swine

Social concerns for environmental impact on air, water and soil pollution have grown along with the accelerated growth of pig production. This study intends to characterize air contamination caused by fungi and particles in swine production, and, additionally, to conclude about their eventual environmental impact. Fiftysix air samples of 50 litters were collected through impaction method. Air sampling and particle matter concentration were performed in indoor and also outdoor premises. Simultaneously, temperature and relative humidity were monitored according to the International Standard ISO 7726 – 1998. Aspergillus versicolor presents the highest indoor spore counts (>2000 CFU/m3) and the highest overall prevalence (40.5%), followed by Scopulariopsis brevicaulis (17.0%) and Penicillium sp. (14.1%). All the swine farms showed indoor fungal species different from the ones identified outdoors and the most frequent genera were also different from the ones indoors. The distribution of particle size showed the same tendency in all swine farms (higher concentration values in PM5 and PM10 sizes). Through the ratio between the indoor and outdoor values, it was possible to conclude that CFU/m3 and particles presented an eventual impact in outdoor measurements.

Entropy diversity in multi-objective particle swarm optimization

Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyzethe MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.

Evaluation of atmospheric deposition and patterns of polycyclic aromatic hydrocarbons in façades of historic monuments of Oporto (Portugal)

Atmospheric pollution by motor vehicles is considered a relevant source of damage to architectural heritage. Thus the aim of this work was to assess the atmospheric depositions and patterns of polycyclic aromatic hydrocarbons (PAHs) in façades of historical monuments. Eighteen PAHs (16 PAHs considered by US EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) were determined in thin black layers collected from façades of two historical monuments: Hospital Santo António and Lapa Church (Oporto, Portugal). Scanning electron microscopy (SEM) was used for morphological and elemental characterisation of thin black layers; PAHs were quantified by microwave-assisted extraction combined with liquid chromatography (MAE-LC). The thickness of thin black layers were 80–110 μm and they contained significant levels of iron, sulfur, calcium and phosphorus. Total concentrations of 18 PAHs ranged from 7.74 to 147.92 ng/g (mean of 45.52 ng/g) in thin black layers of Hospital Santo António, giving a range three times lower than at Lapa Church (5.44– 429.26 ng/g; mean of 110.25 ng/g); four to six rings compounds accounted at both monuments approximately for 80–85% of ΣPAHs. The diagnostic ratios showed that traffic emissions were significant source of PAHs in thin black layers. Composition profiles of PAHs in thin black layers of both monuments were similar to those of ambient air, thus showing that air pollution has a significant impact on the conditions and stone decay of historical building façades. The obtained results confirm that historical monuments in urban areas act as passive repositories for air pollutants present in the surrounding atmosphere.