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%.

Automating the extraction of quantities

The Automated Estimator and LCADesign are two early examples of nD modelling software which both rely on the extraction of quantities from CAD models to support their further processing. The issues of building information modelling (BIM), quantity takeoff for different purposes and automating quantity takeoff are discussed by comparing the aims and use of the two programs. The technical features of the two programs are also described. The technical issues around the use of 3D models is described together with implementation issues and comments about the implementation of the IFC specifications. Some user issues that emerged through the development process are described, with a summary of the generic research tasks which are necessary to fully support the use of BIM and nD modelling.

Simulation of the nutrient supply in fracture healing

The healing process for bone fractures is sensitive to mechanical stability and blood supply at the fracture site. Most currently available mechanobiological algorithms of bone healing are based solely on mechanical stimuli, while the explicit analysis of revascularization and its influences on the healing process have not been thoroughly investigated in the literature. In this paper, revascularization was described by two separate processes: angiogenesis and nutrition supply. The mathematical models for angiogenesis and nutrition supply have been proposed and integrated into an existing fuzzy algorithm of fracture healing. The computational algorithm of fracture healing, consisting of stress analysis, analyses of angiogenesis and nutrient supply, and tissue differentiation, has been tested on and compared with animal experimental results published previously. The simulation results showed that, for a small and medium-sized fracture gap, the nutrient supply is sufficient for bone healing, for a large fracture gap, non-union may be induced either by deficient nutrient supply or inadequate mechanical conditions. The comparisons with experimental results demonstrated that the improved computational algorithm is able to simulate a broad spectrum of fracture healing cases and to predict and explain delayed unions and non-union induced by large gap sizes and different mechanical conditions. The new algorithm will allow the simulation of more realistic clinical fracture healing cases with various fracture gaps and geometries and may be helpful to optimise implants and methods for fracture fixation.

Toward a fuzzy domain ontology extraction method for adaptive e-learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.

Dense Correspondence Extraction in Difficult Uncalibrated Scenarios

The relationship between multiple cameras viewing the same scene may be discovered automatically by finding corresponding points in the two views and then solving for the camera geometry. In camera networks with sparsely placed cameras, low resolution cameras or in scenes with few distinguishable features it may be difficult to find a sufficient number of reliable correspondences from which to compute geometry. This paper presents a method for extracting a larger number of correspondences from an initial set of putative correspondences without any knowledge of the scene or camera geometry. The method may be used to increase the number of correspondences and make geometry computations possible in cases where existing methods have produced insufficient correspondences.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.