Physio-chemical assessment of beauty leaf (Calophyllum inophyllum) as second-generation biodiesel feedstock

Recently, second-generation (non-vegetable oil) feedstocks for biodiesel production are receiving significant attention due to the cost and social effects connected with utilising food products for the production of energy products. The Beauty leaf tree (Calophyllum inophyllum) is a potential source of non-edible oil for producing second-generation biodiesel because of its suitability for production in an extensive variety of atmospheric condition, easy cultivation, high fruit production rate, and the high oil content in the seed. In this study, oil was extracted from Beauty leaf tree seeds through three different oil extraction methods. The important physical and chemical properties of these extracted Beauty leaf oils were experimentally analysed and compared with other commercially available vegetable oils. Biodiesel was produced using a two-stage esterification process combining of an acid catalysed pre-esterification process and an alkali catalysed transesterification process. Fatty acid methyl ester (FAME) profiles and important physicochemical properties were experimentally measured and estimated using equations based on the FAME analysis. The quality of Beauty leaf biodiesels was assessed and compared with commercially available biodiesels through multivariate data analysis using PROMETHEE-GAIA software. The results show that mechanical extraction using a screw press produces oil at a low cost, however, results in low oil yields compared with chemical oil extraction. High pressure and temperature in the extraction process increase oil extraction performance. On the contrary, this process increases the free fatty acid content in the oil. A clear difference was found in the physical properties of Beauty leaf oils, which eventually affected the oil to biodiesel conversion process. However, Beauty leaf oils methyl esters (biodiesel) were very consistent physicochemical properties and able to meet almost all indicators of biodiesel standards. Overall this study found that Beauty leaf is a suitable feedstock for producing second-generation biodiesel in commercial scale. Therefore, the findings of this study are expected to serve as the basis for further development of Beauty leaf as a feedstock for industrial scale second-generation biodiesel production.

Knowledge discovery from tree databases using balanced optimal search

This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.

Crude oil and agricultural futures: An analysis of correlation dynamics

Correlations between oil and agricultural commodities have varied over previous decades, impacted by renewable fuels policy and turbulent economic conditions. We estimate smooth transition conditional correlation models for 12 agricultural commodities and WTI crude oil. While a structural change in correlations occurred concurrently with the introduction of biofuel policy, oil and food price levels are also key influences. High correlation between biofuel feedstocks and oil is more likely to occur when food and oil price levels are high. Correlation with oil returns is strong for biofuel feedstocks, unlike with other agricultural futures, suggesting limited contagion from energy to food markets.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Using Big Data to manage safety-related risk in the upstream oil and gas industry: A research agenda

Despite considerable effort and a broad range of new approaches to safety management over the years, the upstream oil & gas industry has been frustrated by the sector’s stubbornly high rate of injuries and fatalities. This short communication points out, however, that the industry may be in a position to make considerable progress by applying “Big Data” analytical tools to the large volumes of safety-related data that have been collected by these organizations. Toward making this case, we examine existing safety-related information management practices in the upstream oil & gas industry, and specifically note that data in this sector often tends to be highly customized, difficult to analyze using conventional quantitative tools, and frequently ignored. We then contend that the application of new Big Data kinds of analytical techniques could potentially reveal patterns and trends that have been hidden or unknown thus far, and argue that these tools could help the upstream oil & gas sector to improve its injury and fatality statistics. Finally, we offer a research agenda toward accelerating the rate at which Big Data and new analytical capabilities could play a material role in helping the industry to improve its health and safety performance.

Superhydrophobic fabrics for oil-water separation based on the metal organic charge transfer complex CuTCNAQ

This work reports on the fabrication of a superhydrophobic nylon textile based on the organic charge transfer complex CuTCNAQ (TCNAQ = 11,11,12,12-tetracyanoanthraquinodimethane). The nylon fabric that is metallized with copper undergoes a spontaneous chemical reaction with TCNAQ dissolved in acetonitrile to form nanorods of CuTCNAQ that are intertwined over the entire surface of the fabric. This creates the necessary micro and nanoscale roughness that is required for the Cassie-Baxter state thereby achieving a superhydrophobic/superoleophilic surface without the need for a fluorinated surface. The material is characterised with SEM, FT-IR and XPS spectroscopy and investigated for its ability to separate oil and water in two modes, namely under gravity and as an absorbent. It is found that the fabric can separate dichloromethane, olive oil and crude oil from water and in fact reduce the water content of the oil during the separation process. The fabric is reusable and tolerant to conditions such as seawater, hydrochloric acid and extensive time periods on the shelf. Given that CuTCNAQ is a copper based semiconductor may also open up the possibility of other applications in areas such as photocatalysis and antibacterial applications.

Evaluation of oil production from oil palm empty fruit bunch by oleaginous micro-organisms

Oil palm empty fruit bunch (EFB) is a readily available, lignocellulosic biomass that has potential to be utilized as a carbon substrate for microbial oil production. In order to evaluate the production of microbial oil from EFB, a technical study was performed through the cultivation of oleaginous micro-organisms (Rhodotorula mucilaginosa, Aspergillus oryzae, and Mucor plumbeus) on EFB hydrolyzates. EFB hydrolyzates were prepared through dilute acid pre-treatment of the biomass, where the liquid fraction of pre-treatment was detoxified and used as an EFB liquid hydrolyzate (EFBLH). The solid residue was enzymatically hydrolyzed prior to be used as an EFB enzymatic hydrolyzate (EFBEH). The highest oil concentrations were obtained from M. plumbeus (1.9 g/L of oil on EFBLH and 4.7 g/L of oil on EFBEH). In order to evaluate the feasibility of large-scale microbial oil production, a techno-economic study was performed based on the oil yields of M. plumbeus per hectare of plantation, followed by the estimation of the feedstock cost for oil production. Other oil palm biomasses (frond and trunk) were also included in this study, as it could potentially improve the economics of large-scale microbial oil production. Microbial oil from oil palm biomasses was estimated to potentially increase oil production in the palm oil industry up to 25%, at a cheaper feedstock cost. The outcome of this study demonstrates the potential integration of microbial oil production from oil palm biomasses with existing palm oil industry (biodiesel, food and oleochemicals production), that could potentially enhance sustainability and profitability of microbial oil production.

Microbial oil production from sugarcane bagasse hydrolysates by oleaginous yeast and filamentous fungi

This study investigated the potential use of sugarcane bagasse as a feedstock for oil production through microbial cultivation. Bagasse was subjected to dilute acid pretreatment with 0.4 wt% H2SO4 (in liquid) at a solid/liquid ratio of 1:6 (wt/wt) at 170 °C for 15 min, followed by enzymatic hydrolysis of solid residue. The liquid fractions of the pretreatment process and the enzymatic hydrolysis process were detoxified and used as liquid hydrolysate (SCBLH) and enzymatic hydrolysate (SCBEH) for the microbial oil production by oleaginous yeast (Rhodotorula mucilaginosa) and filamentous fungi (Aspergillus oryzae and Mucor plumbeus). The results showed that all strains were able to grow and produce oil from bagasse hydrolysates. The highest oil concentrations produced from bagasse hydrolysates were by M. plumbeus at 1.59 g/L (SCBLH) and 4.74 g/L (SCBEH). The microbial oils obtained have similar fatty acid compositions to vegetable oils, indicating that the oil can be used for the production of second generation biodiesel. On the basis of oil yields obtained by M. plumbeus, from 10 million t (wet weight) of bagasse generated annually from sugar mills in Australia, it is estimated that the total biodiesel that could be produced would be equivalent to about 9% of Queensland’s diesel consumption.

Channelled porous TiO2 synthesized with a water-in-oil microemulsion

Porous titanium dioxide synthesized with a bicontinuous surfactant template is a promising method that leads to a high active surface area electrode. The template used is based on a water/isooctane/dioctyl sodium sulfosuccinate salt together with lecithin. Several parameters were varied during the synthesis to understand and optimize channel formation mechanisms. The material is patterned in stacked conical channels, widening towards the centre of the grains. The active surface area increased by 116% when the concentration of alkoxide precursors was decreased and increased by 241% when the template formation temperature was decreased to 10C. Increasing the oil phase viscosity tends to widen the pore aperture, thus decreasing the overall active surface area. Changing the phase proportions alters the microemulsion integrity and disrupts channel formation.

Comment on “Whole-genome analyses resolve early branches in the tree of life of modern birds”

Jarvis et al. (Research Articles, 12 December 2014, p. 1320) presented molecular clock analyses that suggested that most modern bird orders diverged just after the mass extinction event at the Cretaceous-Paleogene boundary (about 66 million years ago). We demonstrate that this conclusion results from the use of a single inappropriate maximum bound, which effectively precludes the Cretaceous diversification overwhelmingly supported by previous molecular studies.