Biohydrogen Production from Organic Waste – A Review

Hydrogen fuel is a promising alternative to fossil fuels because of its energy content, clean nature, and fuel efficiency. However, it is not readily available. Most current producion processes are very energy intensive and emit carbon dioxide. Therefore, this article reviews technological options for hydrogen production that are eco-friendly and generate clean hydrogen fuel. Biological methods, such different fermentation processes and photolysis are discussed together with the required substrates and the process efficiency.     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

Photo-Chemically-Deposited and Industrial Cu/ZnO/Al2O3 Catalyst Material Surface Structures During CO2 Hydrogenation to Methanol: EXAFS,XANES and XPS Analyses of Phases After Oxidation,Reduction, and Reaction

Catalysis Letters - Industrial Cu/ZnO/Al2O3 or novel rate catalysts, prepared with a photochemical deposition method, were studied under functional CH3OH synthesis conditions at the set temperature...     

Membrane degumming of crude rice bran oil: Pilot plant study

The procedure for the classical chemical refining of vegetable oils consists of degumming, alkali neutralization, bleaching, and deodorization. Conventional refining of rice bran oil using alkali gives oil of acceptable quality, but the refining losses are very high. A critical work has been carried out to study the application of membrane technology in the pretreatment of crude rice bran oil. Oils intended for physical refining should have a low phosphorus content, and this is not readily achievable by the conventional acid/water degumming process. The application of membrane technology for the pretreatment of rice bran oil has been investigated. The process has already been successfully applied to other vegetable oils. Ceramic membranes, which are important from the commercial point of view, were examined for this purpose. The results showed that the membrane‐filtered oils met the requirements of physical refining, with a substantial reduction in color. It was observed that most of the waxy material was also rejected. Experiments were carried out to establish the relationship between permeate flux and rejection with membrane pore size, trans‐membrane pressure and micellar solute concentration.     

Characteristics and compositions ofCarissa spinarum,Leucaena leucocephala andPhysalis minima seeds and oils

The seeds and extracted oils ofCarissa spinarum (Apocynaceae), (I),Leucaena leucocephala (Leguminosae) (II) andPhysalis minima (Solanaceae) (III) were analyzed for characteristics and compositions. The seeds of I, II and III contained 22.4, 6.4 and 40.0% oil and 10.1, 27.6 and 17.9% protein, respectively. The oils of I, II and III had, respectively, iodine values 70.1, 113.5 and 122.5; saponification values 186, 188 and 189; unsaponifiable matter 5.2, 2.5 and 0.8%, and the following fatty acid compositions (area %): palmitic 12.6, 14.2, 10.5; stearic 7.6, 6.1, 8.6; oleic 72.7, 20.1, 17.3; linoleic 5.2, 53.8, 61.4; linolenic 0.9, 1.8, 0.0, and arachidic 1.0, 2.3, 0.0. II contained 1.7% lignoceric acid. III contained small amounts of hexadecenoic (0.1%), epoxy (0.6%) and hydroxy (1.5%) fatty acids.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Language independent unsupervised learning of short message service dialect

Noise in textual data such as those introduced by multilinguality, misspellings, abbreviations, deletions, phonetic spellings, non-standard transliteration, etc. pose considerable problems for text-mining. Such corruptions are very common in instant messenger and short message service data and they adversely affect off-the-shelf text mining methods. Most techniques address this problem by supervised methods by making use of hand labeled corrections. But they require human generated labels and corrections that are very expensive and time consuming to obtain because of multilinguality and complexity of the corruptions. While we do not champion unsupervised methods over supervised when quality of results is the singular concern, we demonstrate that unsupervised methods can provide cost effective results without the need for expensive human intervention that is necessary to generate a parallel labeled corpora. A generative model based unsupervised technique is presented that maps non-standard words to their corresponding conventional frequent form. A hidden Markov model (HMM) over a “subsequencized” representation of words is used, where a word is represented as a bag of weighted subsequences. The approximate maximum likelihood inference algorithm used is such that the training phase involves clustering over vectors and not the customary and expensive dynamic programming (Baum–Welch algorithm) over sequences that is necessary for HMMs. A principled transformation of maximum likelihood based “central clustering” cost function of Baum–Welch into a “pairwise similarity” based clustering is proposed. This transformation makes it possible to apply “subsequence kernel” based methods that model delete and insert corruptions well. The novelty of this approach lies in that the expensive (Baum–Welch) iterations required for HMM, can be avoided through an approximation of the loglikelihood function and by establishing a connection between the loglikelihood and a pairwise distance. Anecdotal evidence of efficacy is provided on public and proprietary data.