Catalytic activity evaluation for hydrogen production via autothermal reforming of methane

A nickel catalyst (5.75 wt.%) supported on gamma-alumina was evaluated through autothermal reforming of methane (ATR). The reforming process was pointed to hydrogen production, following thermodynamic and stoichiometric predictions. The catalyst was characterised by several methods including atomic absorption spectroscopy (AAS), B.E.T.-N₂, X-ray diffraction (XRD), scanning electron microscope (SEM) and thermal analyses (thermogravimetry, TG; derivate thermogravimetry, DTG; and differential thermal analysis, DTA). Experimental evaluations in a fixed-bed reactor (1023–1123 K, 1.00 bar, 150–400 cm³/min feed) presented methane conversions in the range of 40–65%. The effluent mixtures provided hydrogen yields in the range of 78–84%, carbon monoxide 3–14%, and carbon dioxide 5–18%. High molar H₂/CO ratios, ranging from 8 to 90, were obtained. Operating autothermal conditions (excess of steam, 1023–1123 K, 1.00 bar) provided low coke formation and high hydrogen selectivity (81%) for methane reforming.     

Versioned linking of semantic enrichment of legal documents

Regulations affect every aspect of our lives. Compliance with the regulations impacts citizens and businesses similarly: they have to find their rights and obligations in the complex legal environment. The situation is more complex when languages and time versions of regulations should be considered. To propose a solution to these demands, we present a semantic enrichment approach which aims at (1) decreasing the ambiguousness of legal texts, (2) increasing the probability of finding the relevant legal materials, and (3) utilizing the application of legal reasoners. Our approach is also implemented both as a service for citizens and businesses and as a modeling environment for legal drafters. To evaluate the usefulness of the approach, a case study was carried out in a large organization and applied to corporate regulations and Hungarian laws. The results suggest this approach can support the previous aims.     

Dynamic spatial Bayesian models for radioactivity deposition

Dynamic spatial Bayesian (DSB) models are proposed for the analytical modelling of radioactivity deposition after a nuclear accident. The proposed models are extensions of the multi‐variate time‐series dynamic linear models of West and Harrison (1997) to Markov random field processes. They combine the outputs from a long‐range atmospheric dispersal model with measured data (and prior information) to provide improved deposition prediction in space and time. Two versions of a Gaussian DSB model were applied to the radioactivity deposition in Bavaria over a 15 days period during the Chernobyl nuclear accident. One version had fixed functional forms for its spatial variances and covariances while the other allowed those to adapt and ‘learn’ from data in the conjugate Bayesian paradigm. There were two main sources of information for radioactivity deposition in our application: radioactivity measurements at a sparse set of 13 monitoring stations, and the numerical deposition evaluation of the atmospheric dispersal K‐model for the points of a 64 × 64 regular grid. We have analysed the temporal predictions (one‐step‐ahead forecasting) of those DSB models to show that the dispersal K‐model tended in general to underestimate the deposition levels at all times while the DSB models corrected for that although with different degrees of adjustment.     

Optimisation de l'hydrolyse des protéines du lactosérum en utilisant des détergents commerciaux enzymatiques et non-enzymatiques

The main aim of this study is to characterize the effect of some commercial detergents on whey proteins in order to select an efficient detergent to clean inorganic membranes fouled during the process of whey proteins fractionation. The influence of such variables such as time, pH, temperature, type and detergent concentration on the rate of protein hydrolysis has been established. Also, the nature and size of produced peptides have been determined. The most efficient values of cleaning parameters have been obtained by using the enzymatic detergent P3-Ultrasil 62.     

Measuring Water Affordability: A Proposal for Urban Centres in Developed Countries

Although water, at least for basic needs, should be accessible to everyone, many families have trouble paying their water bill. Assessments of water affordability inform the design of water tariffs aimed at ensuring universal access to water. In order to carry out such assessments, managers must have instruments available that accurately reflect the state of water affordability. In this article, a critique is presented of one of the indicators traditionally used to measure water affordability and a proposal of an alternative way of measuring water affordability is put forward. The empirical analysis is applied to data from 301 communities in southern Spain.     

Enhancing the electrical conductivity of carbon black/graphite nanoplatelets: Poly(ethylene‐butyl acrylate) composites by melt extrusion

The influence of processing parameters such as screw geometry, temperature profile, and screw speed on the electrical properties of hybrid composites consisting of graphite nanoplatelets and carbon black in ethyl butyl acrylate was studied. Two different screws were used to compound the hybrid composites at two different temperatures and two different screw speeds. A beneficial effect was noted with regard to the electrical properties when adding nanoplatelets to the filler system. The cause could be a synergistic effect due to the difference in particle shape of the two fillers. Lower percolation thresholds were obtained with the conventional screw due to less breakage of the graphite nanoplatelets compared to the barrier screw. No significant changes of the electrical properties were observed when changing the temperature profiles or the screw speeds. Furthermore, the melt viscosity of the compounds was not appreciably affected at the rather low filler contents used here. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42897.     

Learning Photometric Invariance for Object Detection

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.