Sustainable waste recycling solution for the glass fibre reinforced polymer composite materials industry

In this paper the adequacy and the benefit of incorporating glass fibre reinforced polymer (GFRP) waste materials into polyester based mortars, as sand aggregates and filler replacements, are assessed. Different weight contents of mechanically recycled GFRP wastes with two particle size grades are included in the formulation of new materials. In all formulations, a polyester resin matrix was modified with a silane coupling agent in order to improve binder-aggregates interfaces. The added value of the recycling solution was assessed by means of both flexural and compressive strengths of GFRP admixed mortars with regard to those of the unmodified polymer mortars. Planning of experiments and data treatment were performed by means of full factorial design and through appropriate statistical tools based on analyses of variance (ANOVA). Results show that the partial replacement of sand aggregates by either type of GFRP recyclates improves the mechanical performance of resultant polymer mortars. In the case of trial formulations modified with the coarser waste mix, the best results are achieved with 8% waste weight content, while for fine waste based polymer mortars, 4% in weight of waste content leads to the higher increases on mechanical strengths. This study clearly identifies a promising waste management solution for GFRP waste materials by developing a cost-effective end-use application for the recyclates, thus contributing to a more sustainable fibre-reinforced polymer composites industry.

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.

Polycyclic aromatic hydrocarbons in gas and particulate phases of indoor environments influenced by tobacco smoke: levels, phase distributions, and health risks

As polycyclic aromatic hydrocarbons (PAHs) have a negative impact on human health due to their mutagenic and/or carcinogenic properties, the objective of this work was to study the influence of tobacco smoke on levels and phase distribution of PAHs and to evaluate the associated health risks. The air samples were collected at two homes; 18 PAHs (the 16 PAHs considered by U.S. EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) were determined in gas phase and associated with thoracic (PM10) and respirable (PM2.5) particles. At home influenced by tobacco smoke the total concentrations of 18 PAHs in air ranged from 28.3 to 106 ngm 3 (mean of 66.7 25.4 ngm 3),∑PAHs being 95% higher than at the non-smoking one where the values ranged from 17.9 to 62.0 ngm 3 (mean of 34.5 16.5 ngm 3). On average 74% and 78% of ∑PAHs were present in gas phase at the smoking and non-smoking homes, respectively, demonstrating that adequate assessment of PAHs in air requires evaluation of PAHs in both gas and particulate phases. When influenced by tobacco smoke the health risks values were 3.5e3.6 times higher due to the exposure of PM10. The values of lifetime lung cancer risks were 4.1 10 3 and 1.7 10 3 for the smoking and nonsmoking homes, considerably exceeding the health-based guideline level at both homes also due to the contribution of outdoor traffic emissions. The results showed that evaluation of benzo[a]pyrene alone would probably underestimate the carcinogenic potential of the studied PAH mixtures; in total ten carcinogenic PAHs represented 36% and 32% of the gaseous ∑PAHs and in particulate phase they accounted for 75% and 71% of ∑PAHs at the smoking and non-smoking homes, respectively.

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 he 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.

Particle swarm optimization: dynamical analysis through fractional calculus

This chapter considers the particle swarm optimization algorithm as a system, whose dynamics is studied from the point of view of fractional calculus. In this study some initial swarm particles are randomly changed, for the system stimulation, and its response is compared with a non-perturbed reference response. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behaviour of the best particle. The dynamics is represented through the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence upon the global dynamics is also analyzed. Two main issues are reported: the PSO dynamics when the system is subjected to random perturbations, and its modelling with fractional order transfer functions.

Particle swarm design of digital circuits

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

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.