Assessment and control of nanoparticles exposure in welding operations by use of a control banding tool

This paper describes the use of a Control Banding Tool to assess and further control of exposure of nanoparticles emitted during welding operations. The tool was applied to Metal Active Gas (MAG) arc welding of mild and stainless steel, providing semi-quantitative data on the process, so that protection measures could be derived, e.g. exhaust gas ventilation by hoods, local ventilation devices and containment measures. This tool is quite useful to compare and evaluate the characteristics of arc welding procedures so that more eco-friendly processes could be preferred over the more potentially noxious ones.

Trace metals in size-fractionated particulate matter in a Portuguese hospital: exposure risks assessment and comparisons with other countries

Hospitals are considered as a special and important type of indoor public place where air quality has significant impacts on potential health outcomes. Information on indoor air quality of these environments, concerning exposures to particulate matter (PM) and related toxicity, is limited though. This work aims to evaluate risks associated with inhalation exposure to ten toxic metals and chlorine (As, Ni, Cr, Cd, Pb, Mn, Se, Ba, Al, Si, and Cl) in coarse (PM2.5–10) and fine (PM2.5) particles in a Portuguese hospital in comparison with studies representative of other countries. Samples were collected during 1 month in one urban hospital; elemental PM characterization was determined by proton-induced X-ray emission. Noncarcinogenic and carcinogenic risks were assessed according to the methodology provided by the United States Environmental Protection Agency (USEPA; Region III Risk-Based Concentration Table) for three different age categories of hospital personnel (adults, >20, and <65 years) and patients (considering nine different age groups, i.e., children of 1–3 years to seniors of >65 years). The estimated noncarcinogenic risks due to occupational inhalation exposure to PM2.5-bound metals ranged from 5.88×10−6 for Se (adults, 55–64 years) to 9.35×10−1 for As (adults, 20–24 years) with total noncarcinogenic risks (sum of all metals) above the safe level for all three age categories. As and Cl (the latter due to its high abundances) were the most important contributors (approximately 90 %) to noncarcinogenic risks. For PM2.5–10, noncarcinogenic risks of all metals were acceptable to all age groups. Concerning carcinogenic risks, for Ni and Pb, they were negligible (<1×10−6) in both PM fractions for all age groups of hospital personnel; potential risks were observed for As and Cr with values in PM2.5 exceeding (up to 62 and 5 times, respectively) USEPA guideline across all age groups; for PM2.5–10, increased excess risks of As and Cr were observed particularly for long-term exposures (adults, 55–64 years). Total carcinogenic risks highly (up to 67 times) exceeded the recommended level for all age groups, thus clearly showing that occupational exposure to metals in fine particles pose significant risks. If the extensive working hours of hospital medical staff were considered, the respective noncarcinogenic and carcinogenic risks were increased, the latter for PM2.5 exceeding the USEPA cumulative guideline of 10−4. For adult patients, the estimated noncarcinogenic and carcinogenic risks were approximately three times higher than for personnel, with particular concerns observed for children and adolescents.

Assessment of ultrafine particles in Portuguese preschools: levels and exposure doses

The aim of this work was to assess ultrafine particles (UFP) number concentrations in different microenvironments of Portuguese preschools and to estimate the respective exposure doses of UFP for 3–5-year-old children (in comparison with adults). UFP were sampled both indoors and outdoors in two urban (US1, US2) and one rural (RS1) preschool located in north of Portugal for 31 days. Total levels of indoor UFP were significantly higher at the urban preschools (mean of 1.82x104 and 1.32x104 particles/cm3 at US1 an US2, respectively) than at the rural one (1.15x104 particles/cm3). Canteens were the indoor microenvironment with the highest UFP (mean of 5.17x104, 3.28x104, and 4.09x104 particles/cm3 at US1, US2, and RS1), whereas the lowest concentrations were observed in classrooms (9.31x103, 11.3x103, and 7.14x103 particles/cm3 at US1, US2, and RS1). Mean indoor/outdoor ratios (I/O) of UFP at three preschools were lower than 1 (0.54–0.93), indicating that outdoor emissions significantly contributed to UFP indoors. Significant correlations were obtained between temperature, wind speed, relative humidity, solar radiation, and ambient UFP number concentrations. The estimated exposure doses were higher in children attending urban preschools; 3–5-year-old children were exposed to 4–6 times higher UFP doses than adults with similar daily schedules.

Reply to: Comments on “Particle Swarm Optimization with Fractional-Order Velocity”

We agree with Ling-Yun et al. [5] and Zhang and Duan comments [2] about the typing error in equation (9) of the manuscript [8]. The correct formula was initially proposed in [6, 7]. The formula adopted in our algorithms discussed in our papers [1, 3, 4, 8] is, in fact, the following: ...

Gelatin particle indirect agglutination test for serodiagnosis of schistosomiasis: comparative study with enzyme-linked immunosorbent assay

A new serological test, the gelatin particle agglutination test (GPAT), was used for the serodiagnosis of schistosomiasis mansoni. This technique showed the sensitivity (90.6%) and specificity (97.8%) close to those of enzyme-linked immunosorbent assay. The GPAT can be easily and rapidly performed without specialized equipment, by using lyophilized antigen-coated gelatin particles. The test also seems to be useful for mass screening of Schistosoma infection in field conditions.

Studies on human anti-rabies immunization in Brazil: I - Evaluation of the 3 + 1 pre-exposure vaccination schedule under field conditions

The currently used pre-exposure anti-rabies immunization schedule in Brazil is the one called 3+1, employing suckling mouse brain vaccine (3 doses on alternate days and the last one on day 30). Although satisfactory results were obtained in well controlled experimental groups using this immunization schedule, in our routine practice, VNA levels lower than 0.5 IU/ml are frequently found. We studied the pre-exposure 3+1 schedule under field conditions in different cities on the State of São Paulo, Brazil, under variable and sometimes adverse circumstances, such as the use of different batches of vaccine with different titers, delivered, stored and administered under local conditions. Fifty out of 256 serum samples (19.5%) showed VNA titers lower than 0.5 IU/ml, but they were not distributed homogeneously among the localities studied. While in some cities the results were completely satisfactory, in others almost 40% did not attain the minimum VNA titer required. The results presented here, considered separately, question our currently used procedures for human pre-exposure anti-rabies immunization. The reasons determining this situation are discussed.

Studies on human anti-rabies immunization in Brazil: II - Preliminary evaluation of the 2-1-1 schedule for human pre-exposure anti-rabies immunization, employing suckling mouse brain vaccine

This study reports preliminary results of virus neutralizing antibody (VNA) titers obtained on different days in the course of human anti-rabies immunization with the 2-1-1 schedule (one dose is given in the right arm and one dose in the left arm at day 0, and one dose is apllied on days 7 and 21), recommended by WHO for post-exposure treatment with cell culture vaccines. A variant schedule (double dose on day zero and another on day 14) was also tested, both employing suckling mouse brain vaccine. A complete seroconversion rate was obtained after only 3 vaccine doses, and almost all patients (11 of 12) presented titers higher than 1.0 IU/ml. Both neutralizing response and seroconversion rates were lower in the group receiving only 3 doses, regardless of the sample collecting day. Although our results are lower than those found with cell culture vaccines, the geometry mean of VNA is fully satisfactory, overcoming the lower limit recommended by WHO of 0.5 IU/ml. The 2-1-1 schedule could be an alternative one for pre exposure immunization, shorter than the classical 3+1 regimen (one dose on days 0, 2, 4 and 30) with only three visits to the doctor, instead of four.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.