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.     

Auditory motivated level-crossing approach to instantaneous frequency estimation

We address the problem of estimating the instantaneous frequency (IF) of a phase signal using its level-crossing (LC) information based on front-end auditory processing motivation. We show that the problem of IF estimation using LC information can be cast in the framework of estimation from irregularly sampled data. The formulation has the generality of estimating different types of IF without the need for a quasistationary assumption. We consider two types of IF-polynomial and bandlimited; we use polynomial interpolating functions for the former, and for the latter, we propose a novel "line plus sum of sines" model. The model parameters are estimated by linear regression. Considering the noisy case, LC data for different levels is analyzed, and methods for combining different estimators from LCs are discussed. Theoretical and extensive simulation results show that the performance of the zero-crossing (ZC) based IF estimator and the level-crossing based IF estimator with smaller level values is better than those obtained with higher level values or their combinations. The new technique reaches the Crame/spl acute/r-Rao bound (CRB) roughly above 4 dB signal-to-noise ratio (SNR), and its performance does not deteriorate rapidly with mismatch in the IF order compared with the other techniques in the literature.     

Instability due to Viscosity Stratification Downstream of a Centerline Injector

A common injector geometry upstream of a static mixer is the centerline injector. A flow instability can arise due to viscosity differences between the injected core‐flow and the outer co‐flow. This instability can adversely affect the effectiveness of the mixing operation. An experimental investigation of miscible viscosity‐stratified flow in a circular geometry was performed using Laser Induced Fluorescence (LIF) and Particle Image Velocimetry (PIV). The experimental results for the stable region agree with the analytical results. The unstable region exhibits different modes depending on the viscosity ratio, volume flux ratio, and Reynolds number. The modes include wavy core‐flow with fissures and wavy core‐flow with core breakup. The time‐averaged experiment velocity profiles for the unstable core indicate a broadening of the jet at the centerline, which is consistent with the LIF visualization.     

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.     

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.

     

Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate–distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.     

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.