https://openscholar.dut.ac.za/bitstream/10321/1400/1/rawat__kumar__mutanda___bux_2013_aplplied_energy.pdf

Biodiesel from microalgae: A critical evaluation from laboratory to large scale production

Flocculation as a low-cost method for harvesting microalgae for bulk biomass production.

The economically significant production of carbon-neutral biodiesel from microalgae has been hailed as the ultimate alternative to depleting resources of petro-diesel due to its high cellular concentration of lipids, resources and economic sustainability and overall potential advantages over other sources of biofuels. Pertinent questions however need to be answered on the commercial viability of large scale production of biodiesel from microalgae. Vital steps need to be critically analysed at each stage. Isolation of microalgae should be based on the question of whether marine or freshwater microalgae, cultures from collections or indigenous wild types are best suited for large scale production. Furthermore, the determination of initial sampling points play a pivotal role in the determination of strain selection as well as strain viability. The screening process should identify, purify and select lipid producing strains. Are natural strains or stressed strains higher in lipid productivity? The synergistic interactions that occur naturally between algae and other microorganisms cannot be ignored. A lot of literature is available on the downstream processing of microalgae but a few reports are available on the upstream processing of microalgae for biomass and lipid production for biodiesel production. We present in this review an empirical and critical analysis on the potential of translating research findings from laboratory scale trials to full scale application. The move from laboratory to large scale microalgal cultivation requires careful planning. It is imperative to do extensive pre-pilot demonstration trials and formulate a suitable trajectory for possible data extrapolation for large scale experimental designs. The pros and cons of the two widely used methods for growing microalgae by photobioreactors or open raceway ponds are discussed in detail. In addition, current methods for biomass harvesting and lipid extraction are critically evaluated. This would be novel approach to economical biodiesel production from microalgae in the near future. Globally, microalgae are largest biomass producers having higher neutral lipid content outcompeting terrestrial plants for biofuel production. However, the viscosities of microalgal oils are usually higher than that of petroleum diesel.

Biodiesel from microalgae: a critical evaluation from laboratory to large scale production. Appl Energy

Economic and technical problems related to the reduction of petroleum resources require the valorisation of renewable raw material Recently, microalgae emerged as promising alternative feedstock that represents an enormous biodiversity with multiple benefits exceeding the potential of conventional agricultural feedstock Thus, this comprehensive review article spots the light on one of the most interesting microalga Chlorella vulgaris It assembles the history and a thorough description of its ultrastructure and composition according to growth conditions The harvesting techniques are presented in relation to the novel algo-refinery concept, with their technological advancements and potential applications in the market

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Morphology, composition, production, processing and applications of Chlorella vulgaris: A review

Modern life is intimately linked to the availability of fossil fuels, which continue to meet the world's growing energy needs even though their use drives climate change, exhausts finite reserves and contributes to global political strife. Biofuels made from renewable resources could be a more sustainable alternative, particularly if sourced from organisms, such as algae, that can be farmed without using valuable arable land. Strain development and process engineering are needed to make algal biofuels practical and economically viable.

Exploiting diversity and synthetic biology for the production of algal biofuels

Evaluation of flocculation induced by pH increase for harvesting microalgae and reuse of flocculated medium

Image hashing retrieval based on generative adversarial networks

Abstract With the availability of numerous high-resolution remote sensing images, remote sensing image scene classification has been widely used in various fields. Compared with the field of natural images, the insufficient number of labeled remote sensing images limits the performance of supervised scene classification, while unsupervised methods are difficult to meet the practical applications. Therefore, this paper proposes a semi-supervised remote sensing image scene classification method using generative adversarial networks. The proposed method introduces dense residual block, pre-trained Inception V3 networks, gating unit, pyramidal convolution, and spectral normalization into GANs to promote the semi-supervised classification performance. To be specific, the pre-trained Inception V3 network is introduced to extract semantic features to enhance the feature discriminant capability. The gating unit is utilized to capture the relationships among features. The pyramidal convolution is integrated into dense residual block to capture different levels of details to strengthen the feature representation capability. The spectral normalization is introduced to stabilize the GANs training to improve semi-supervised classification accuracy. Extensive experimental results on publicly available EuroSAT and UC Merced datasets show that the proposed method gains the highest overall accuracy, especially when only a few labeled samples are available. 

/pdf/semi-supervised-remote-sensing-image-scene-classification-fh4x9ewd.pdf

Semi-supervised Remote Sensing Image Scene Classification Based on Generative Adversarial Networks

Image classification and recognition has a very wide range of applications in computer vision, which involves many fields, such as image retrieval, image analysis, and robot positioning. Especially with the rise of brain science and cognitive science research, as well as the increasing diversification of imaging means, three-dimensional image data mainly based on magnetic resonance image plays an increasingly important role in image classification and recognition, especially in medical image classification and recognition. However, due to the high dimensional characteristics of human magnetic resonance images, human readability is reduced. Therefore, classification and recognition of 3-dimensional images is still a challenge. In order to better extract local features from images and effectively use their spatial information, this paper improved the “feature bag” and “spatial pyramid matching” algorithms on the basis of 3D feature extraction algorithm and proposed an image classification framework based on 3D feature extraction algorithm. Firstly, the multiresolution “3D spatial pyramid” algorithm, the multiscale image segmentation and image representation method, and the SVM classifier and feature fusion method are described. Secondly, the gender information contained in the magnetic resonance images is classified and recognized on the three databases selected in the experiment. Experimental results show that this method can effectively utilize the spatial information of three-dimensional images and achieve satisfactory results in the classification and recognition of human magnetic resonance images.

/pdf/research-on-image-classification-and-key-technologies-based-2myylkxj.pdf

Zechen Wu

Papers

Evaluation of flocculation induced by pH increase for harvesting microalgae and reuse of flocculated medium

Image hashing retrieval based on generative adversarial networks

Semi-supervised Remote Sensing Image Scene Classification Based on Generative Adversarial Networks

Research on Image Classification and Key Technologies Based on 3D Feature Extraction Algorithm