Air contaminants in animal production: the poultry case

A descriptive study was developed in order to assess air contamination caused by fungi and particles in seven poultry units. Twenty seven air samples of 25 litters were collected through impaction method. Air sampling and particle concentration measurement were performed in the pavilions’ interior and also outside premises, since this was the place regarded as reference. Simultaneously, temperature and relative humidity were also registered. Regarding fungal load in the air from the seven poultry farms, the highest value obtained was 24040 CFU/m3 and the lowest was 320 CFU/m3. Twenty eight species/genera of fungi were identified, being Scopulariopsis brevicaulis (39.0%) the most commonly isolated species and Rhizopus sp. (30.0%) the most commonly isolated genus. From the Aspergillus genus, Aspergillus flavus (74.5%) was the most frequently detected species. There was a significant correlation (r=0.487; p=0.014) between temperature and the level of fungal contamination (CFU/m3). Considering contamination caused by particles, in this study, particles with larger dimensions (PM5.0 and PM10) have higher concentrations. There was also a significant correlation between relative humidity and concentration of smaller particles namely, PM0.5 (r=0.438; p=0.025) and PM1.0 (r=0.537; p=0.005). Characterizing typical exposure levels to these contaminants in this specific occupational setting is required to allow a more detailed risk assessment analysis and to set exposure limits to protect workers’ health.

Occupational exposure to aflatoxin (AFB1) in poultry production

Aflatoxin B1 (AFB1) has been recognized to produce cancer in human liver. In addition, epidemiological and laboratory studies demonstrated that the respiratory system was a target for AFB1. Exposure occurs predominantly through the food chain, but inhalation represents an additional route of exposure. The present study aimed to examine AFB1 exposure among poultry workers in Portugal. Blood samples were collected from a total of 31 poultry workers from six poultry farms. In addition, a control group (n = 30) was included comprised of workers who undertook administrative tasks. Measurement of AFB1 in serum was performed by enzyme-linked immunosorbent assay (ELISA). For examining fungi contamination, air samples were collected through an impaction method. Air sampling was obtained in pavilion interior and outside the premises, since this was the place regarded as the reference location. Using molecular methods, toxicogenic strains (aflatoxin-producing) were investigated within the group of species belonging to Aspergillus flavus complex. Eighteen poultry workers (59%) had detectable levels of AFB1 with values ranging from <1 ng/ml to4.23 ng/ml and with a mean value of 2 ± 0.98ng/ml. AFB1 was not detected in the serum sampled from any of the controls. Aspergillus flavus was the fungal species third most frequently found in the indoor air samples analyzed (7.2%) and was the most frequently isolated species in air samples containing only Aspergillus genus (74.5%). The presence of aflatoxigenic strains was only confirmed in outdoor air samples from one of the units, indicating the presence of a source inside the building in at least one case. Data indicate that AFB1 inhalation represents an additional risk in this occupational setting that needs to be recognized, assessed, and prevented.

Biological monitoring of aflatoxin (AFB1) in workers of swine and poultry production

Although a great body of literature exists concerning the ingestion of food contaminated with aflatoxin, there are still few studies regarding mycotoxin inhalation in occupational settings. Since mycotoxins are relatively non-volatile, inhalation exposure is cause by inhalation of airborne fungal particulates or fungi-contaminated substrates that contain aflatoxin. We intend to know if there is occupational exposure to aflatoxin in Portuguese poultry and swine production. A total of 19 individuals (11 swine; 8 poultry) agreed and provided blood samples during the course of this investigation. Measurement of AFB1 was performed by ELISA. The samples were treated with pronase (Merck), wash in a Column C18 and purification was made with immunoaffinity columns (R.biopharma), specific for AFB1. It was applied statistical test (Mann-Whitney) to verified statistical difference in AFB1 results between the two settings. Results varied with concentrations from

A new cloning system based on the OprI lipoprotein for the production of recombinant bacterial cell wall-derived immunogenic formulations

The conjugation of antigens with ligands of pattern recognition receptors (PRR) is emerging as a promising strategy for the modulation of specific immunity. Here, we describe a new Escherichia coli system for the cloning and expression of heterologous antigens in fusion with the OprI lipoprotein, a TLR ligand from the Pseudomonas aeruginosa outer membrane (OM). Analysis of the OprI expressed by this system reveals a triacylated lipid moiety mainly composed by palmitic acid residues. By offering a tight regulation of expression and allowing for antigen purification by metal affinity chromatography, the new system circumvents the major drawbacks of former versions. In addition, the anchoring of OprI to the OM of the host cell is further explored for the production of novel recombinant bacterial cell wall-derived formulations (OM fragments and OM vesicles) with distinct potential for PRR activation. As an example, the African swine fever virus ORF A104R was cloned and the recombinant antigen was obtained in the three formulations. Overall, our results validate a new system suitable for the production of immunogenic formulations that can be used for the development of experimental vaccines and for studies on the modulation of acquired immunity.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

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]

Reentrant Phase Diagram of Network Fluids

We introduce a microscopic model for particles with dissimilar patches which displays an unconventional "pinched'' phase diagram, similar to the one predicted by Tlusty and Safran in the context of dipolar fluids [Science 290, 1328 (2000)]. The model-based on two types of patch interactions, which account, respectively, for chaining and branching of the self-assembled networks-is studied both numerically via Monte Carlo simulations and theoretically via first-order perturbation theory. The dense phase is rich in junctions, while the less-dense phase is rich in chain ends. The model provides a reference system for a deep understanding of the competition between condensation and self-assembly into equilibrium-polymer chains.

Study of distribution network demand response events in the Portuguese system

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. Grid operators and utilities are taking new initiatives, recognizing the value of demand response for grid reliability and for the enhancement of organized spot markets’ efficiency. This paper proposes a methodology for the selection of the consumers that participate in an event, which is the responsibility of the Portuguese transmission network operator. The proposed method is intended to be applied in the interruptibility service implemented in Portugal, in convergence with Spain, in the context of the Iberian electricity market. This method is based on the calculation of locational marginal prices (LMP) which are used to support the decision concerning the consumers to be schedule for participation. The proposed method has been computationally implemented and its application is illustrated in this paper using a 937 bus distribution network with more than 20,000 consumers.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Maximum penetration of microgeneration photovoltaic panels in distribution network

In smart grids context, the distributed generation units based in renewable resources, play an important rule. The photovoltaic solar units are a technology in evolution and their prices decrease significantly in recent years due to the high penetration of this technology in the low voltage and medium voltage networks supported by governmental policies and incentives. This paper proposes a methodology to determine the maximum penetration of photovoltaic units in a distribution network. The paper presents a case study, with four different scenarios, that considers a 32-bus medium voltage distribution network and the inclusion storage units.

Smart Electric Vehicle Charging System

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. Due to the electrical power distribution network limitation and absence of smart meter devices, Electric Vehicles charging should be performed in a balanced way, taking into account past experience, weather information based on data mining, and simulation approaches. In order to allow information exchange and to help user mobility, it was also created a mobile application to assist the EV driver on these processes. This proposed Smart ElectricVehicle Charging System uses Vehicle-to-Grid (V2G) technology, in order to connect Electric Vehicles and also renewable energy sources to Smart Grids (SG). This system also explores the new paradigm of Electrical Markets (EM), with deregulation of electricity production and use, in order to obtain the best conditions for commercializing electrical energy.

Decision support system with incomplete and domain incoherent information management

Many of the most common human functions such as temporal and non-monotonic reasoning have not yet been fully mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This is mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, recognition of chronicles, fuzzy expert systems, etc. SPARSE (from the Portuguese acronym, which means expert system for incident analysis and restoration support) was one of the developed systems and, in the sequence of its development, came the need to cope with incomplete and/or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA (supervisory control and data acquisition) information retrieval, namely real-time operation, huge amounts of information, etc. This paper presents an architecture for a decision support system, which can solve the presented problems, using a symbiosis of the event calculus and the default reasoning rule based system paradigms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability. A prototype implementation of this system is already at work in the control centre of the Portuguese Transmission Network.

Study on the use of MgAl hydrotalcites as solid heterogeneous catalysts for biodiesel production

This paper, reports experimental work on the use of new heterogeneous solid basic catalysts for biodiesel production: double oxides of Mg and Al, produced by calcination, at high temperature, of MgAl lamellar structures, the hydrotalcites (HT). The most suitable catalyst system studied are hydrotalcite Mg:Al 2:1 calcinated at 507 degrees C and 700 degrees C, leading to higher values of FAME also in the second reaction stage. One of the prepared catalysts resulted in 97.1% Fatty acids methyl esters (FAME) in the 1st reaction step, 92.2% FAME in the 2nd reaction step and 34% FAME in the 3rd reaction step. The biodiesel obtained in the transesterification reaction showed composition and quality parameters within the limits specified by the European Standard EN 14214. 2.5% wt catalyst/oil and a molar ratio methanol:oil of 9:1 or 12:1 at 60 -65 degrees C and 4 h of reaction time are the best operating conditions achieved in this study. This study showed the potential of Mg/Al hydrotalcites as heterogeneous catalysts for biodiesel production. (C) 2011 Elsevier Ltd. All rights reserved.