Particle Swarm algorithm with Fuzzy decision making for a multi-objective economic and environmental optimization of design of a thermal system

Multi-Objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the thermoeconomic and Environmental aspects have been considered, simultaneously. The environmental objective function has been defined and expressed in cost terms. One of the most suitable optimization techniques developed using a particular class of search algorithms known as; Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used here. This approach has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of fuzzy decision-making with the aid of Bellman-Zadeh approach has been presented and a final optimal solution has been introduced.

Simulation of Au particle interaction on graphene sheets

The interaction of Au particles with few layer graphene is of interest for the formation of the next generation of sensing devices(1). In this paper we investigate the coupling of single gold nanoparticles to a graphene sheet, and multiple gold nanoparticles with a graphene sheet using COMSOL Multiphysics. By using these simulations we are able to determine the electric field strength and associated hot-spots for various gold nanoparticle-graphene systems. The Au nanoparticles were modelled as 8 nm diameter spheres on 1.5 nm thick (5 layers) graphene, with properties of graphene obtained from the refractive index data of Weber(2) and the Au refractive index data from Palik(3). The field was incident along the plane of the sheet with polarisation tested for both s and p. The study showed strong localised interaction between the Au and graphene with limited spread; however the double particle case where the graphene sheet separated two Au nanoparticles showed distinct interaction between the particles and graphene. An offset was introduced (up to 4 nm) resulting in much reduced coupling between the opposed particles as the distance apart increased. Findings currently suggest that the graphene layer has limited interaction with incident fields with a single particle present whilst reducing the coupling region to a very fine area when opposing particles are involved. It is hoped that the results of this research will provide insight into graphene-plasmon interactions and spur the development of the next generation of sensing devices.

Numerical simulations of particle deposition in metal foam heat exchangers

Australia is a high-potential country for geothermal power with reserves currently estimated in the tens of millions of petajoules, enough to power the nation for at least 1000 years at current usage. However, these resources are mainly located in isolated arid regions where water is scarce. Therefore, wet cooling systems for geothermal plants in Australia are the least attractive solution and thus air-cooled heat exchangers are preferred. In order to increase the efficiency of such heat exchangers, metal foams have been used. One issue raised by this solution is the fouling caused by dust deposition. In this case, the heat transfer characteristics of the metal foam heat exchanger can dramatically deteriorate. Exploring the particle deposition property in the metal foam exchanger becomes crucial. This paper is a numerical investigation aimed to address this issue. Two dimensional (2D) numerical simulations of a standard one-row tube bundle wrapped with metal foam in cross-flow are performed and highlight preferential particle deposition areas.

Numerical simulations of particle deposition in metal foam heat exchangers

Australia is a high potential country for geothermal power with reserves currently estimated in the tens of millions of petajoules, enough to power the nation for at least 1000 years at current usage.However, these resources are mainly located in isolated arid regions where water is scarce. Therefore, wet cooling systems for geothermal plants in Australia are the least attractive solution and thus air-cooled heat exchangers are preferred. In order to increase the efficiency of such heat exchangers, metal foams have been used. One issue raised by this solution is the fouling caused by dust deposition. In this case, the heat transfer characteristics of the metal foam heat exchanger can dramatically deteriorate. Exploring the particle deposition property in the metal foam exchanger becomes crucial. This paper is a numerical investigation aimed to address this issue. Two-dimensional(2D numerical simulations of a standard one-row tube bundle wrapped with metal foam in cross-flow are performed and highlight preferential particle deposition areas.

A modified particle swarm optimization for correlated phenomena

The wide applicability of correlation analysis inspired the development of this paper. In this paper, a new correlated modified particle swarm optimization (COM-PSO) is developed. The Correlation Adjustment algorithm is proposed to recover the correlation between the considered variables of all particles at each of iterations. It is shown that the best solution, the mean and standard deviation of the solutions over the multiple runs as well as the convergence speed were improved when the correlation between the variables was increased. However, for some rotated benchmark function, the contrary results are obtained. Moreover, the best solution, the mean and standard deviation of the solutions are improved when the number of correlated variables of the benchmark functions is increased. The results of simulations and convergence performance are compared with the original PSO. The improvement of results, the convergence speed, and the ability to simulate the correlated phenomena by the proposed COM-PSO are discussed by the experimental results.

Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO)

This paper presents a novel algorithm based on particle swarm optimization (PSO) to estimate the states of electric distribution networks. In order to improve the performance, accuracy, convergence speed, and eliminate the stagnation effect of original PSO, a secondary PSO loop and mutation algorithm as well as stretching function is proposed. For accounting uncertainties of loads in distribution networks, pseudo-measurements is modeled as loads with the realistic errors. Simulation results on 6-bus radial and 34-bus IEEE test distribution networks show that the distribution state estimation based on proposed DLM-PSO presents lower estimation error and standard deviation in comparison with algorithms such as WLS, GA, HBMO, and original PSO.

Thermal stability and decomposition kinetics of styrene-butadiene rubber nanocomposites filled with different particle sized kaolinites

A series of styrene-butadiene rubber (SBR) nanocomposites filledwith different particle sized kaolinites are prepared via a latex blending method. The thermal stabilities of these clay polymer nanocomposites (CPN) are characterized by a range of techniques including thermogravimetry (TG), digital photos, scanning electron microscopy (SEM) and Raman spectroscopy. These CPN show some remarkable improvement in thermal stability compared to that of the pure SBR. With the increase of kaolinite particle size, the residual char content and the average activation energy of kaolinite SBR nanocomposites all decrease; the pyrolysis residues become porous; the crystal carbon in the pyrolysis residues decrease significantly from 58.23% to 44.41%. The above results prove that the increase of kaolinite particle size is not beneficial in improving the thermal stability of kaolinite SBR nanocomposites.

Insight into morphology and structure of different particle sized kaolinites with same origin

The particle size, morphology, crystallinity order and structural defects of four kaolinite samples are characterized by the techniques including particle size analysis, scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR) and magic angle spinning nuclear magnetic resonance spectroscopy (MAS NMR). The particle size of four kaolinite samples gradually increases. Four samples all belong to the ordered kaolinite and show a decrease in structural order with the increase of kaolinite particle size. The changes of structural defect are proved by the increase of the band splitting in Raman spectroscopy, the decrease of the intensity of absorption bands in infrared spectroscopy, and the decrease of equivalent silicon atom and the increase of nonequivalent aluminum atom in MAS NMR spectroscopy. The differences in morphology and structural defect are attributed to the broken bonds of Al–O–Si, Al–O–Al and Si–O–Si and the Al substitution for Si in tetrahedral sheets.

The impact of flood and post-flood cleaning on airborne microbiological and particle contamination in residential houses

In January 2011, Brisbane, Australia, experienced a major river flooding event. We aimed to investigate its effects on air quality and assess the role of prompt cleaning activities in reducing the airborne exposure risk. A comprehensive, multi-parameter indoor and outdoor measurement campaign was conducted in 41 residential houses, 2 and 6 months after the flood. The median indoor air concentrations of supermicrometer particle number (PN), PM10, fungi and bacteria 2 months after the flood were comparable to those previously measured in Brisbane. These were 2.88 p cm-3, 15 µg m-3, 804 cfu m-3 and 177 cfu m-3 for flood-affected houses (AFH), and 2.74 p cm-3, 15 µg m-3, 547 cfu m-3 and 167 cfu m-3 for non-affected houses (NFH), respectively. The I/O (indoor/outdoor) ratios of these pollutants were 1.08, 1.38, 0.74 and 1.76 for AFH and 1.03, 1.32, 0.83 and 2.17 for NFH, respectively. The average of total elements (together with transition metals) in indoor dust was 2296 ± 1328 µg m-2 for AFH and 1454 ± 678 µg m-2 for NFH, respectively. In general, the differences between AFH and NFH were not statistically significant, implying the absence of a measureable effect on air quality from the flood. We postulate that this was due to the very swift and effective cleaning of the flooded houses by 60,000 volunteers. Among the various cleaning methods, the use of both detergent and bleach was the most efficient at controlling indoor bacteria. All cleaning methods were equally effective for indoor fungi. This study provides quantitative evidence of the significant impact of immediate post-flood cleaning on mitigating the effects of flooding on indoor bioaerosol contamination and other pollutants.

Characteristics of ultrafine particle sources and deposition rates in primary school classrooms

The aim of this work was to investigate changes in particle number concentration (PNC) within naturally ventilated primary school classrooms arising from local sources either within or adjacent to the classrooms. We quantify the rate at which ultrafine particles were emitted either from printing, grilling, heating or cleaning activities and the rate at which the particles were removed by both deposition and air exchange processes. At each of 25 schools in Brisbane, Australia, two weeks of measurements of PNC and CO2 were taken both outdoors and in the two classrooms. Bayesian regression modelling was employed in order to estimate the relevant rates and analyse the relationship between air exchange rate (AER), particle infiltration and the deposition rates of particle generated from indoor activities in the classrooms. During schooling hours, grilling events at the school tuckshop as well as heating and printing in the classrooms led to indoor PNCs being elevated by a factor of more than four, with emission rates of (2.51 ± 0.25) x 1011 p min-1, (8.99 ± 6.70) x 1011 p min-1 and (5.17 ± 2.00) x 1011 p min-1, respectively. During non-school hours, cleaning events elevated indoor PNC by a factor of above five, with an average emission rate of (2.09 ± 6.30) x 1011 p min-1. Particles were removed by both air exchange and deposition; chiefly by ventilation when AER > 0.7 h-1 and by deposition when AER < 0.7 h-1.

A particle based model to simulate microscale morphological changes of plant tissues during drying

Fundamental understanding on microscopic physical changes of plant materials is vital to optimize product quality and processing techniques, particularly in food engineering. Although grid-based numerical modelling can assist in this regard, it becomes quite challenging to overcome the inherited complexities of these biological materials especially when such materials undergo critical processing conditions such as drying, where the cellular structure undergoes extreme deformations. In this context, a meshfree particle based model was developed which is fundamentally capable of handling extreme deformations of plant tissues during drying. The model is built by coupling a particle based meshfree technique: Smoothed Particle Hydrodynamics (SPH) and a Discrete Element Method (DEM). Plant cells were initiated as hexagons and aggregated to form a tissue which also accounts for the characteristics of the middle lamella. In each cell, SPH was used to model cell protoplasm and DEM was used to model the cell wall. Drying was incorporated by varying the moisture content, the turgor pressure, and cell wall contraction effects. Compared to the state of the art grid-based microscale plant tissue drying models, the proposed model can be used to simulate tissues under excessive moisture content reductions incorporating cell wall wrinkling. Also, compared to the state of the art SPH-DEM tissue models, the proposed model better replicates real tissues and the cell-cell interactions used ensure efficient computations. Model predictions showed good agreement both qualitatively and quantitatively with experimental findings on dried plant tissues. The proposed modelling approach is fundamentally flexible to study different cellular structures for their microscale morphological changes at dehydration.

Relating urban airborne particle concentrations to shipping using carbon based elemental emission ratios

This study demonstrates a novel method for testing the hypothesis that variations in primary and secondary particle number concentration (PNC) in urban air are related to residual fuel oil combustion at a coastal port lying 30 km upwind, by examining the correlation between PNC and airborne particle composition signatures chosen for their sensitivity to the elemental contaminants present in residual fuel oil. Residual fuel oil combustion indicators were chosen by comparing the sensitivity of a range of concentration ratios to airborne emissions originating from the port. The most responsive were combinations of vanadium and sulfur concentration ([S], [V]) expressed as ratios with respect to black carbon concentration ([BC]). These correlated significantly with ship activity at the port and with the fraction of time during which the wind blew from the port. The average [V] when the wind was predominantly from the port was 0.52 ng.m-3 (87%) higher than the average for all wind directions and 0.83 ng.m-3 (280%) higher than that for the lowest vanadium yielding wind direction considered to approximate the natural background. Shipping was found to be the main source of V impacting urban air quality in Brisbane. However, contrary to the stated hypothesis, increases in PNC related measures did not correlate with ship emission indicators or ship traffic. Hence at this site ship emissions were not found to be a major contributor to PNC compared to other fossil fuel combustion sources such as road traffic, airport and refinery emissions.

Performance and gaseous and particle emissions from a liquefied petroleum gas (LPG) fumigated compression ignition engine

In this study, an LPG fumigation system was fitted to a Euro III compression ignition (CI) engine to explore its impact on performance, and gaseous and particulate emissions. LPG was introduced to the intake air stream (as a secondary fuel) by using a low pressure fuel injector situated upstream of the turbocharger. LPG substitutions were test mode dependent, but varied in the range of 14-29% by energy. The engine was tested over a 5 point test cycle using ultra low sulphur diesel (ULSD), and a low and high LPG substitution at each test mode. The results show that LPG fumigation coerces the combustion into pre-mixed mode, as increases in the peak combustion pressure (and the rate of pressure rise) were observed in most tests. The emissions results show decreases in nitric oxide (NO) and particulate matter (PM2.5) emissions; however, very significant increases in carbon monoxide (CO) and hydrocarbon (HC) emissions were observed. A more detailed investigation of the particulate emissions showed that the number of particles emitted was reduced with LPG fumigation at all test settings – apart from mode 6 of the ECE R49 test cycle. Furthermore, the particles emitted generally had a slightly larger median diameter with LPG fumigation, and had a smaller semi-volatile fraction relative to ULSD. Overall, the results show that with some modifications, LPG fumigation systems could be used to extend ULSD supplies without adversely impacting on engine performance and emissions.

Suppression of cluster ions during particle formation events in the atmosphere

Cluster ions and charged and neutral nanoparticle concentrations were monitored using a neutral cluster and air ion spectrometer (NAIS) over a period of one year in Brisbane, Australia. The study yielded 242 complete days of usable data, of which particle formation events were observed on 101 days. Small, intermediate and large ion concentrations were evaluated in real time. In the diurnal cycle, small ion concentration was highest during the second half of the night while large ion concentrations were a maximum during the day. The small ion concentration showed a decrease when the large ion concentration increased. Particle formation was generally followed by a peak in the intermediate ion concentration. The rate of increase of intermediate ions was used as the criteria for identifying particle formation events. Such events were followed by a period of growth to larger sizes and usually occurred between 8 am and 2 pm. Particle formation events were found to be related to the wind direction. The gaseous precursors for the production of secondary particles in the urban environment of Brisbane have been shown to be ammonia and sulfuric acid. During these events, the nanoparticle number concentrations in the size range 1.6 to 42 nm, which were normally lower than 1x104 cm-3, often exceeded 5x104 cm-3 with occasional values over 1x105 cm-3. Cluster ions generally occurred in number concentrations between 300 and 600 cm-3 but decreased significantly to about 200 cm-3 during particle formation events. This was accompanied by an increase in the large ion concentration. We calculated the fraction of nanoparticles that were charged and investigated the occurrence of possible overcharging during particle formation events. Overcharging is defined as the condition where the charged fraction of particles is higher than in charge equilibrium. This can occur when cluster ions attach to neutral particles in the atmosphere, giving rise to larger concentrations of charged particles in the short term. Ion-induced nucleation is one of the mechanisms of particle formation in the atmosphere, and overcharging has previously been considered as an indicator of this process. The possible role of ions in particle formation was investigated.

A meshfree particle based model for microscale shrinkage mechanisms of food materials in drying conditions

Cells are the fundamental building block of plant based food materials and many of the food processing born structural changes can fundamentally be derived as a function of the deformations of the cellular structure. In food dehydration the bulk level changes in porosity, density and shrinkage can be better explained using cellular level deformations initiated by the moisture removal from the cellular fluid. A novel approach is used in this research to model the cell fluid with Smoothed Particle Hydrodynamics (SPH) and cell walls with Discrete Element Methods (DEM), that are fundamentally known to be robust in treating complex fluid and solid mechanics. High Performance Computing (HPC) is used for the computations due to its computing advantages. Comparing with the deficiencies of the state of the art drying models, the current model is found to be robust in replicating drying mechanics of plant based food materials in microscale.