Evaluation of a pilot-scale oil extraction from microalgae for biodiesel production

Biodiesel derived from microalgae is one of a suite of potential solutions to meet the increasing demand for a renewable, carbon-neutral energy source. However, there are numerous challenges that must be addressed before algae biodiesel can become commercially viable. These challenges include the economic feasibility of harvesting and dewatering the biomass and the extraction of lipids and their conversion into biodiesel. Therefore, it is essential to find a suitable extraction process given these processes presently contribute significantly to the total production costs which, at this stage, inhibit the ability of biodiesel to compete financially with petroleum diesel. This study focuses on pilot-scale (100 kg dried microalgae) solvent extraction of lipids from microalgae and subsequent transesterification to biodiesel. Three different solvents (hexane, isopropanol (IPA) and hexane + IPA (1:1)) were used with two different extraction methods (static and Soxhlet) at bench-scale to find the most suitable solvent extraction process for the pilot-scale. The Soxhlet method extracted only 4.2% more lipid compared to the static method. However, the fatty acid profiles of different extraction methods with different solvents are similar, suggesting that none of the solvents or extraction processes were biased for extraction of particular fatty acids. Considering the cost and availability of the solvents, hexane was chosen for pilot-scale extraction using static extraction. At pilot-scale the lipid yield was found to be 20.3% of total biomass which is 2.5% less than from bench scale. Extracted fatty acids were dominated by polyunsaturated fatty acids (PUFAs) (68.94±0.17%) including 47.7±0.43 and 17.86±0.42% being docosahexaenoic acid (DHA) (C22:6) and docosapentaenoic acid (DPA) (C22:5, ω-3), respectively. These high amounts of long chain poly unsaturated fatty acids are unique to some marine microalgae and protists and vary with environmental conditions, culture age and nutrient status, as well as with cultivation process. Calculated physical and chemical properties of density, viscosity of transesterified fatty acid methyl esters (FAMEs) were within the limits of the biodiesel standard specifications as per ASTM D6751-2012 and EN 14214. The calculated cetane number was, however, significantly lower (17.8~18.6) compared to ASTM D6751-2012 or EN 14214-specified minimal requirements. We conclude that the obtained microalgal biodiesel would likely only be suitable for blending with petroleum diesel to a maximum of 5 to 20%.

High temperature reduces apple fruit colour via modulation of the anthocyanin regulatory complex

The biosynthesis of anthocyanin in many plants is affected by environmental conditions. In apple (Malus×domestica Borkh.), concentrations of fruit anthocyanins are lower under hot climatic conditions. We examined the anthocyanin accumulation in the peel of maturing 'Mondial Gala' and 'Royal Gala' apples, grown in both temperate and hot climates, and using artificial heating of on-tree fruit. Heat caused a dramatic reduction of both peel anthocyanin concentration and transcripts of the genes of the anthocyanin biosynthetic pathway. Heating fruit rapidly reduced expression of the R2R3 MYB transcription factor (MYB10) responsible for coordinative regulation for red skin colour, as well as expression of other genes in the transcriptional activation complex. A single night of low temperatures is sufficient to elicit a large increase in transcription of MYB10 and consequently the biosynthetic pathway. Candidate genes that can repress anthocyanin biosynthesis did not appear to be responsible for reductions in anthocyanin content. We propose that temperature-induced regulation of anthocyanin biosynthesis is primarily caused by altered transcript levels of the activating anthocyanin regulatory complex.

On evaluation of robust autonomy for uninhabited vehicles and systems

This paper discusses a method to quantify robust autonomy of Uninhabited Vehicles and Systems (UVS) in aerospace, marine, or land applications. Based on mission-vehicle specific performance criteria, we define an system utility function that can be evaluated using simulation scenarios for an envelope of environmental conditions. The results of these evaluations are used to compute a figure of merit or measure for operational efectiveness (MOE). The procedure is then augmented to consider faults and the performance of mechanisms to handle these faulty operational modes. This leads to a measure of robust autonomy (MRA). The objective of the proposed figures of merit is to assist in decision making about vehicle performance and reliability at both vehicle development stage (using simulation models) and at certification stage (using hardware-in-the-loop testing). Performance indices based on dynamic and geometric tasks associated with vehicle manoeuvring problems are proposed, and an example of a two- dimensional y scenario is provided to illustrate the use of the proposed figures of merit.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Improved image set classification via joint sparse approximated nearest subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).

Partitioning the variation in stream fish assemblages within a spatio-temporal hierarchy

This paper describes the relative influence of: (i) landscape scale environmental and hydrological factors; (ii) local scale environmental conditions including recent flow history, and; (iii) spatial effects (proximity of sites to one another) on the spatial and temporal variation in local freshwater fish assemblages in the Mary River, south-eastern Queensland, Australia. Using canonical correspondence analysis, each of the three sets of variables explained similar amounts of variation in fish assemblages (ranging from 44 to 52%). Variation in fish assemblages was partitioned into eight unique components: pure environmental, pure spatial, pure temporal, spatially structured environmental variation, temporally structured environmental variation, spatially structured temporal variation, the combined spatial/temporal component of environmental variation and unexplained variation. The total variation explained by these components was 65%. The combined spatial/temporal/environmental component explained the largest component (30%) of the total variation in fish assemblages, whereas pure environmental (6%), temporal (9%) and spatial (2%) effects were relatively unimportant. The high degree of intercorrelation between the three different groups of explanatory variables indicates that our understanding of the importance to fish assemblages of hydrological variation (often highlighted as the major structuring force in river systems) is dependent on the environmental context in which this role is examined.

Accurately defining the reference condition for summary biotic metrics: A comparison of four approaches

Protocols for bioassessment often relate changes in summary metrics that describe aspects of biotic assemblage structure and function to environmental stress. Biotic assessment using multimetric indices now forms the basis for setting regulatory standards for stream quality and a range of other goals related to water resource management in the USA and elsewhere. Biotic metrics are typically interpreted with reference to the expected natural state to evaluate whether a site is degraded. It is critical that natural variation in biotic metrics along environmental gradients is adequately accounted for, in order to quantify human disturbance-induced change. A common approach used in the IBI is to examine scatter plots of variation in a given metric along a single stream size surrogate and a fit a line (drawn by eye) to form the upper bound, and hence define the maximum likely value of a given metric in a site of a given environmental characteristic (termed the 'maximum species richness line' - MSRL). In this paper we examine whether the use of a single environmental descriptor and the MSRL is appropriate for defining the reference condition for a biotic metric (fish species richness) and for detecting human disturbance gradients in rivers of south-eastern Queensland, Australia. We compare the accuracy and precision of the MSRL approach based on single environmental predictors, with three regression-based prediction methods (Simple Linear Regression, Generalised Linear Modelling and Regression Tree modelling) that use (either singly or in combination) a set of landscape and local scale environmental variables as predictors of species richness. We compared the frequency of classification errors from each method against set biocriteria and contrast the ability of each method to accurately reflect human disturbance gradients at a large set of test sites. The results of this study suggest that the MSRL based upon variation in a single environmental descriptor could not accurately predict species richness at minimally disturbed sites when compared with SLR's based on equivalent environmental variables. Regression-based modelling incorporating multiple environmental variables as predictors more accurately explained natural variation in species richness than did simple models using single environmental predictors. Prediction error arising from the MSRL was substantially higher than for the regression methods and led to an increased frequency of Type I errors (incorrectly classing a site as disturbed). We suggest that problems with the MSRL arise from the inherent scoring procedure used and that it is limited to predicting variation in the dependent variable along a single environmental gradient.

Development and application of a predictive model of freshwater fish assemblage composition to evaluate river health in eastern Australia

Multivariate predictive models are widely used tools for assessment of aquatic ecosystem health and models have been successfully developed for the prediction and assessment of aquatic macroinvertebrates, diatoms, local stream habitat features and fish. We evaluated the ability of a modelling method based on the River InVertebrate Prediction and Classification System (RIVPACS) to accurately predict freshwater fish assemblage composition and assess aquatic ecosystem health in rivers and streams of south-eastern Queensland, Australia. The predictive model was developed, validated and tested in a region of comparatively high environmental variability due to the unpredictable nature of rainfall and river discharge. The model was concluded to provide sufficiently accurate and precise predictions of species composition and was sensitive enough to distinguish test sites impacted by several common types of human disturbance (particularly impacts associated with catchment land use and associated local riparian, in-stream habitat and water quality degradation). The total number of fish species available for prediction was low in comparison to similar applications of multivariate predictive models based on other indicator groups, yet the accuracy and precision of our model was comparable to outcomes from such studies. In addition, our model developed for sites sampled on one occasion and in one season only (winter), was able to accurately predict fish assemblage composition at sites sampled during other seasons and years, provided that they were not subject to unusually extreme environmental conditions (e.g. extended periods of low flow that restricted fish movement or resulted in habitat desiccation and local fish extinctions).

Endurance test-rig for diagnostics and prognostics of rolling element bearings

Detection of faults in roller element bearing is a topic widely discussed in the scientific field. Bearings diagnostics is usually performed by analyzing experimental signals, almost always vibration signals, measured during operation. A number of signal processing techniques have been proposed and applied to measured vibrations. The diagnostic effectiveness of the techniques depends on their capacities and on the environmental conditions (i.e. environmental noise). The current trend, especially from an industrial point of view, is to couple the prognostics to the diagnostics. The realization of a prognostic procedure require the definition of parameters able to describe the bearing condition during its operation. Monitoring the values of these parameters during time allows to define their trends depending on the progress of the wear. In this way, a relation between the variation of the selected parameters and the wear progress, useful for diagnostics and prognostics of bearings in real industrial applications, can be established. In this paper, a laboratory test-rig designed to perform endurance tests on roller element bearing is presented. Since the test-rig has operated for a short time, only some preliminary available results are discussed.

An experimental based assessment of the deviation of the bearing characteristic frequencies

Slippage in the contact roller-races has always played a central role in the field of diagnostics of rolling element bearings. Due to this phenomenon, vibrations triggered by a localized damage are not strictly periodic and therefore not detectable by means of common spectral functions as power spectral density or discrete Fourier transform. Due to the strong second order cyclostationary component, characterizing these signals, techniques such as cyclic coherence, its integrated form and square envelope spectrum have proven to be effective in a wide range of applications. An expert user can easily identify a damage and its location within the bearing components by looking for particular patterns of peaks in the output of the selected cyclostationary tool. These peaks will be found in the neighborhood of specific frequencies, that can be calculated in advance as functions of the geometrical features of the bearing itself. Unfortunately the non-periodicity of the vibration signal is not the only consequence of the slippage: often it also involves a displacement of the damage characteristic peaks from the theoretically expected frequencies. This issue becomes particularly important in the attempt to develop highly automated algorithms for bearing damage recognition, and, in order to correctly set thresholds and tolerances, a quantitative description of the magnitude of the above mentioned deviations is needed. This paper is aimed at identifying the dependency of the deviations on the different operating conditions. This has been possible thanks to an extended experimental activity performed on a full scale bearing test rig, able to reproduce realistically the operating and environmental conditions typical of an industrial high power electric motor and gearbox. The importance of load will be investigated in detail for different bearing damages. Finally some guidelines on how to cope with such deviations will be given, accordingly to the expertise obtained in the experimental activity.

Experimental evidences in bearing diagnostics for traction system of high speed trains

Rolling element bearings are the most critical components in the traction system of high speed trains. Monitoring their integrity is a fundamental operation in order to avoid catastrophic failures and to implement effective condition based maintenance strategies. Generally, diagnostics of rolling element bearings is usually performed by analyzing vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. Several papers have been published on this subject in the last two decades, mainly devoted to the development and assessment of signal processing techniques for diagnostics. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in specific industrial applications, particularly in rail industry, remains scarcely investigated. This paper is aimed at filling this knowledge gap, by addressing the diagnostics of bearings taken from the service after a long term operation on the traction system of a high speed train. Moreover, in order to test the effectiveness of the diagnostic procedures in the environmental conditions peculiar to the rail application, a specific test-rig has been built, consisting of a complete full-scale train traction system, able to reproduce the effects of wheeltrack interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is proposed, in order to limit their number.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

Integrating the impact of climate into event based failure models for water pipes

This paper presents an event-based failure model to predict the number of failures that occur in water distribution assets. Often, such models have been based on analysis of historical failure data combined with pipe characteristics and environmental conditions. In this paper weather data have been added to the model to take into account the commonly observed seasonal variation of the failure rate. The theoretical basis of existing logistic regression models is briefly described in this paper, along with the refinements made to the model for inclusion of seasonal variation of weather. The performance of these refinements is tested using data from two Australian water authorities.

Straminipilan protists : a study of their taxonomic position, morphology and abiotic stress-mediated gene expression

Thraustochytrids have become of considerable industrial and scientific interest in the past decade due to their health benefits. They have been proven to be the principle source in marine and estuarine fish diets with high percentage of long chain (LC) or polyunsaturated fatty acids (PUFA). Therefore, the oil extracted from fish for human document.forms[0].elements[13].select();consumption is rich in PUFA with high omega-3 fatty acid content. Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) of all of the omega-3 fatty acids, are considered beneficial essential oils for humans with a wide range of health benefits. These include brain and neural development in infants, general wellbeing of adults and drug delivery through precursor molecules. They have become one of the most extensively studied organisms for industrial oil preparations as PUFA extraction from fish becomes less profitable. Many forms of these Thraustochytrid oils are being trialled for human consumption all over the world. In Australia, there has been little research performed on these organisms in the past ten years. A few Australian studies have been conducted in the form of comparative studies related to PUFA production within the related genera, but not focussed on their identification or cellular and genomic characterisation. Therefore, the main aim of this study was to investigate the morphological and genetic characteristics of Australian Thraustochytrids in order to aid in their identification and characterisation, as well as to better understand the effect of environmental conditions in the regulation of PUFA production. It was also noted that there was a knowledge gap in the preservation and total genomic DNA extraction of these organisms for the purposes of scientific research. The cryopreservation of these organisms for studies around the world follows existing generic methods. However, it is well understood that many of these generic methods attract not only high costs for chemicals, but also uses considerable storage space and other resources, all of which can be improved with new or modified approaches. In this context, a simple and inexpensive bead preservation method is described, without compromising the storage shelf life. We also describe, for the first time, the effects of culture age on the successful cryopreservation of Thraustochytrids. It was evident in the literature that DNA and RNA extractions for molecular and genetic studies of Thraustochytrids follow the classical phenol-chloroform extraction methods. It was also observed that modern protocols failed to avoid the use of phenol-chloroform rather than improving preparation and cell disruption. In order to provide a high quantity and quality DNA extraction, a modified protocol has been introduced that employs the use of modern commercial extraction kits and standard laboratory equipment. Thraustochytrids have been shown to be highly conserved in their 18S rDNA gene sequences, which is used as the current standard for identification. It was demonstrated that the 18S rDNA gene sequence limits the recognition of closely related genera or within the genera from each member. Therefore, it was proposed that another profile, such as a randomly amplified polymorphic DNA (RAPD) based profiling system, be tested for use in the characterisation of Thraustochytrids. The RAPD profiles were shown to provide a unique DNA fingerprint for each isolate and small variations in their genome were able to be detected. This method involved the use of a minimum number of standard arbitrary primers and with an increase in the number of different primers used, a very high discrimination between organisms could be achieved. However, the method was not suitable for taxonomic purposes because the results did not correlate with other taxonomic features such as morphology. Another knowledge gap was found with respect to Australian Thraustochytrid growth characteristics, in that these had not been recorded and published. In order to rectify this, a record of colony and microscopic features of 12 selected isolates was performed. The results of preliminary studies indicated that further microbiological and biochemical studies are needed for full characterisation of these organisms. This information is of great importance to bio-prospecting of new Thraustochytrids from Australian ecosystems and would allow for their accurate identification, and so permit the prediction of their PUFA capability by comparison with related genera/species. It was well recognized that environmental stress plays a role in the PUFA production and is mainly due to the reactive oxygen species as abiotic stress (Chiou et al., 2001; Okuyama et al., 2008; Shabala et al., 2009; Shabala et al., 2001). In this aspect, this study makes the first attempt towards better understanding of this phenomenon by way of the use of real-time PCR for the detection of environmental effects on the regulation of PUFA production. Three main environmental conditions including temperature, pH and oxygen availability were monitored as stress inducers. In summary, this study provides novel approaches for the preservation and handling of Thraustochytrids, their molecular biological features, taxonomy, characterisation and responses to environmental factors with respect to their oil production enzymes. The information produced from this study will prove to be vital for both industrial and scientific investigations in the future.

Advances in gyro-stabilisation of vessel roll motion

The use of gyro-dynamic forces to counteract the wave-induced roll motion of marine vessels in a seaway was proposed over 100 years ago. These early systems showed a remarkable performance, reporting roll reductions of up to 95% in some sailing conditions. Despite this success, further developments were not pursued since the systems were unable to provide acceptable performance over an extended envelope of sailing and environmental conditions, and the invention of fin roll stabilisers provided a satisfactory alternative. This has been attributed to simplistic controls, heavy drive systems, and large structural mass required to withstand the loads given the low strength materials available at the time. Today, advances in material strength, bearings, motor technology and mechanical design methods, together with powerful signal processing algorithms, has resulted in a revitalized interest in gyro-stabilisers for ships. Advanced control systems have enabled optimisation of restoring torques across a range of wave environments and sailing conditions through adaptive control system design. All of these improvements have resulted in increased spinning speed, lower mass, and dramatically increased stabilising performance. This brief paper provides an overview of recent developments in the design and control of gyro-stabilisers of ship roll motion. In particular, the novel Halcyon Gyro-Stabilisers are introduced, and their performance is illustrated based on a simulation case study for a naval patrol vessel. Given the growing national and global interest in small combatants and patrol vessels, modem gyro-stabilisers may offer a significant technological contribution to the age old problem of comfort and mission operability on small ships, especially at loiter speeds.