Characterising vehicle emissions from the burning of biodiesel made from vegetable oil

Biodiesel manufactured from canola oil was blended with diesel and used as fuel in two diesel vehicles. This study aimed to test the emissions of diesel engines using blends of 100%, 80%, 60%, 40% , 20% biodiesel and 100% petroleum diesel, and characterise the particulate matter and gaseous emissions, with particular attention to levels of polycyclic aromatic hydrocarbons (PAHs) which are harmful to humans. A real time dust monitor was also used to monitor the continuous dust emissions during the entire testing cycle. The ECE(Euro 2) drive cycle was used for all emission tests. It was found that the particle concentration was up to 33% less when the engine burnt 100% biodiesel, compared to 100% diesel. Particle emission reduced with increased percentages of biodiesel in the fuel blends. Reductions of NOx, HC and CO were limited to about 10% when biodiesel was burned. Levels of CO2 emissions from the use of biodiesel and diesel were similar. Eighteen EPA priority PAHs were targeted, with only 6 species detected in the gaseous phase from the samples . 9 PAHs were detected in particulate phases at much lower levels than gaseous PAHs. Some marked reductions were observed for less toxic gaseous PAHs such as naphthalene when burning 100% biodiesel, but the particulate PAH emissions, which have more implications to adverse health effects, were virtually unchanged and did not show a statistically significant reduction. These findings are useful to gain an understanding of the emissions and environmental impacts of biodiesel.

Effect of particle characteristics on the two-body abrasive wear behaviour of a pearlitic steel

The specific wear rate and friction coefficient of a pearlitic microstructure subjected to different abrasive environments (i.e. SiC and alumina) were examined. A CSM high temperature pin-on-disc tribometer was used to simulate the two-body abrasive condition (i.e. the metallic surface abrading against the abrasive particles). The characteristics of the abrasive particles (i.e. particle size and density) revealed a significant impact on the amount of material loss. The specific wear rate of the pearlitic microstructure decreased with a reduction in the abrasive particle size, irrespective of the particle type. In addition, distinct particle deterioration mechanisms were observed during the abrasion process, which was largely determined by the abrasive particle size. Attrition, shelling and fracture were some of the dominant particle deterioration mechanisms occurring in both of the abrasive environments. SEM and EDX analysis on the wear debris displayed a unique metallic chip formation with respect to the particle type. Furthermore, the abrading efficiency (i.e. threshold level) of the abrasive particles was identified by means of interrupted abrasive wear tests. The dense packing nature of the alumina abrasive particles resulted in a significantly higher material removal rate than the SiC abrasive environment.

Polycyclic aromatic hydrocarbon emission loads and profiles from the burning of locally grown wood species in Victoria, Australia - Some pilot project results

A testing facility for combustion of biomass and sampling of emissions has been established at Deakin University. In this pilot project using this facility, four kinds of locally grown wood species were burned and the particle emissions sampled and analysed for Polycyclic Aromatic Hydrocarbons (PAHs). The selected wood species covering pine, red gum, yellow box and sugar gum, are the most popular domestic fuel wood in Australia. Particulate matter emissions from burning of each load of wood were sampled from the flue using a standard stack emission sampling train. The particle-laden filters were extracted and the .extract analysed to determine PAH concentrations by Gas Chromatographyl Mass Spectrometer (Gc/MS). The sampling was conducted under two different burning conditions with the air inlet of the combustion chamber fully open and with it half open. A suite of 15 PAHs, ranging from naphthalene (C IOHB) to dibenzolahlanthracene (C12H14), were selected for analysis. PAH profiles for the four wood species, under the different burning regimes, have been generated. Some preliminary emission factors for the different wood species have been derived as microgram of summed PAHs (rPAHs) emittedlkilogram of wood burned. Total Particulate Matter (TPM) emission factors were also obtained from gravimetric measurement of the sample filter before and after the combustion. Based on these emission factors, pine displayed the highest level of rPAHs emitted from the combustion of the four wood species, with sugar gum showing the lowest level of rPAHs emission. Emission factors associated with the slow burning condition clearly showed higher l:PAH levels compared to the faster burning condition. During the faster burning condition, red gum and pine show a higher percentage of rPAH to TPM than sugar gum or yellow box. Under the slower burning. the l:PAHlTPM ratio in every case was greater.

Frictional behaviour and wear performance of a nitrocarburised coating sliding against AISI 1019 steel

This work employed a commercial nitrocaburising process to diffuse a coating onto M2 grade high speed tool steel. Properties of the nitrocaburised coating (CN) such as thickness, roughness and hardness were characterised using a variety of techniques including Glow-Discharge Optical Emission Spectrometry (GD-OES) and Scanning Electron Microscopy (SEM). A tribological test has been developed in which two nominally identical crossed cylinders slide over each other under selected test conditions. The test has been employed to investigate the wear performance of both CN coated and uncoated M2 specimens and frictional behaviour of the sliding interface between the tool and a AISI 1019 steel workpiece under unlubricated (dry) and lubricated conditions. Fourier Transform Infrared Spectroscopy (FTIR) was used to monitor the formation of chemical species from the oxidation of lubricant during tribological testing.

Particle dynamics and heat transfer at workpiece surface in heat treatment fluidised beds

The particle behaviour is studied by the analysis of particle images taken with a high speed CCD digital video camera. The comparison of particle dynamics is performed for the fluidised beds without part, with single part and with multi-parts. The results show that there are significant differences in particle behaviours both in different beds and at different locations at part surfaces. The total and radiative heat transfer coefficients at different surfaces of a metallic component in a high temperature fluidised bed are measured by a heat transfer probe developed in the present work. The principle of the heat transfer probe is to measure the change in temperature of the heated metallic piece with time and, then, to extract the heat flux and heat transfer coefficients. The structure of the probe is optimized with numerical simulation of energy conservation for measuring the heat transfer coefficient of 150~600 W/m2 K. The relationship between the particle dynamics and the heat transfer is analysed to form the basis for future more rational designs of fluidised beds as well as for improved quality control.

Particle trapping using dielectrophoretically patterned carbon nanotubes

This study presents the dielectrophoretic (DEP) assembly of multi-walled carbon nanotubes (MWCNTs) between curved microelectrodes for the purpose of trapping polystyrene microparticles within a microfluidic system. Under normal conditions, polystyrene particles exhibit negative DEP behaviour and are repelled from microelectrodes. Interestingly, the addition of MWCNTs to the system alters this situation in two ways: first, they coat the surface of particles and change their dielectric properties to exhibit positive DEP behaviour; second, the assembled MWCNTs are highly conductive and after the deposition serve as extensions to the microelectrodes. They establish an array of nanoelectrodes that initiates from the edge of microelectrodes and grow along the electric field lines. These nanoelectrodes can effectively trap the MWCNT-coated particles, since they cover a large portion of the microchannel bottom surface and also create a much stronger electric field than the primary microelectrodes as confirmed by our numerical simulations. We will show that the presence of MWCNT significantly changes performance of the system, which is investigated by trapping sample polystyrene particles with plain, COOH and goat anti-mouse IgG surfaces.

Animal–plant–microbe interactions : direct and indirect effects of swan foraging behaviour modulate methane cycling in temperate shallow wetlands

Wetlands are among the most important ecosystems on Earth both in terms of productivity and biodiversity, but also as a source of the greenhouse gas CH₄. Microbial processes catalyzing nutrient recycling and CH₄ production are controlled by sediment physico-chemistry, which is in turn affected by plant activity and the foraging behaviour of herbivores. We performed field and laboratory experiments to evaluate the direct effect of herbivores on soil microbial activity and their indirect effects as the consequence of reduced macrophyte density, using migratory Bewick’s swans (Cygnus columbianus bewickii Yarrell) feeding on fennel pondweed (Potamogeton pectinatus L.) tubers as a model system. A controlled foraging experiment using field enclosures indicated that swan bioturbation decreases CH₄ production, through a decrease in the activity of methanogenic Archaea and an increased rate of CH₄ oxidation in the bioturbated sediment. We also found a positive correlation between tuber density (a surrogate of plant density during the previous growth season) and CH₄ production activity. A laboratory experiment showed that sediment sterilization enhances pondweed growth, probably due to elimination of the negative effects of microbial activity on plant growth. In summary, the bioturbation caused by swan grazing modulates CH₄ cycling by means of both direct and indirect (i.e. plant-mediated) effects with potential consequences for CH₄ emission from wetland systems.

Particle dynamics and heat transfer at workpiece surface in heat treatment fluidised beds

The particle behaviour in a heat treatment fluidised bed was studied by the analysis of particle images taken with a high speed CCD digital video camera. The comparison of particle dynamics was performed for the fluidised beds without part, with single part and with multi-parts. The results show that there are significant differences in particle behaviours both in different beds and at different locations of part surfaces. The total and radiative heat transfer coefficients at different surfaces of a metallic part in a fluidised bed were measured by a heat transfer probe developed in the present work. The structure of the probe was optimized with numerical simulation of energy conservation for measuring the heat transfer coefficient of 150-600 W/m2K. The relationship between the particle dynamics and the heat transfer was analysed to form the basis for future more rational designs of fluidised beds as well as for improved quality control.

Wind-thermal systems operation optimization considering emission problem

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Moreover, comparisons are performed with other optimization algorithms. The simulation results show that our hybrid method is better in terms of the speed and accuracy. The main contribution of this paper is the development of a emission unit commitment model integrating with wind energy and combining the SQP and PSO methods to achieve faster and better performance optimization

Evolution of strain-induced precipitates in a model austenitic Fe-30Ni-Nb steel and their effect on the flow behaviour

The present work investigated the evolution of strain-induced NbC precipitates in a model austenitic Fe-30Ni-Nb steel deformed at 925 °C to a strain of 0.2 during post-deformation holding between 3 and 1000 s and their effect on the reloading flow stress. The precipitate particles preferentially nucleated on the nodes of the periodic dislocation networks constituting microband walls. Holding for 10 s resulted in the formation of fine, largely coherent NbC particles with a mean diameter of ∼5 nm, which displayed a cube-on-cube orientation relationship with austenite and caused the maximum increase in the reloading steady-state flow stress. A further increase in the holding time from 30 to 1000 s led to the formation of semi-coherent, gradually coarser and more widely spaced particles with a mean diameter of 8 nm and above, which led to a gradual decrease in the reloading steady-state flow stress. The holding time increase resulted in progressive disintegration of the dislocation substructure and dislocation annihilation through static recovery processes, which was also reflected by the measured softening fractions. The precipitate particle shape changed during post-deformation annealing from elliptical to faceted octahedral and subsequently to tetra-kai-decahedral. © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Driver behaviour prediction for motion simulators using changepoint segmentation

Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.