Evaluation of Atmospheric and Vacuum Residues Using Molecular Distillation and Optimization

The term atmospheric residue describes the material at the bottom of the atmospheric distillation tower having a lower boiling point limit of about 340°C; the term vacuum residue (heavy petroleum fractions) refers to the bottom of the vacuum distillation, which has an atmospheric equivalent boiling point (AEBP) above 540°C. In this work, the objective is to evaluate the behavior of different kinds of Brazilian atmospheric and vacuum residues using molecular distillation. The Falling Film Molecular Distillator was used. For the results obtained through this process, a significant range of temperature can be explored avoiding the thermal decomposition of the material. So these results are very important to the refinery decisions and improvements. The Experimental Factorial Design results showed that the temperature has more influence on the process than the feed flow rate, when a higher percentage of distillate is required.     

Lexically scoped distribution: what you see is what you get

We define a lexically scoped, asynchronous and distributed π-calculus, with local communication and process migration. This calculus adopts the network-awareness principle for distributed programming and follows a simple model of distribution for mobile calculi: a lexical scope discipline combines static scoping with dynamic linking, associating channels to a fixed site throughout computation. This discipline provides for both remote invocation and process migration. A simple type system is a straightforward extension of that of the π-calculus, adapted to take into account the lexical scope of channels. An equivalence law captures the essence of this model: a process behavior depends on the channels it uses, not on where it runs.     

From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval

The paradigm for image retrieval has evolved from low-level image representations to semantic concept models to higher-level semantic inferences. UCSD's Statistical Visual Computing Laboratory has developed effective techniques for each paradigm that equate retrieval with classification and strive for minimum-probability-of-error optimality.     

A New Triazole-Thiol Compound Organofunctionalized on the Silica Gel Surface: Chemical Properties and Copper Sorption in Ethanol / Water Media

Silicon - This study describes the preparation and characterization of 3-chloropropyl silica gel with 4-amino-5-(4pyridyl)-4H-1,2,4-triazole-3-thiol (SGA) for copper adsorption in different media....     

Surface Pretreatments of Silicon Nitride for CVD Diamond Deposition

The efficiency of different surface pretreatments (four standard chemical etchings and four diamond powder abrasive procedures) on silicon nitride (Si₃N₄) substrates for chemical vapor deposition (CVD) of diamond has been systematically investigated. Blank Si₃N₄ samples were polished with colloidal silica (∼0.25 μm). Diamond nucleation and growth runs were conducted in a microwave plasma chemical vapor deposition apparatus for 10 min and 6 h, respectively. Superior results concerning nucleation density ( N _d∼ 10¹⁰ cm⁻² after 10 min), film uniformity, and grain size (below 2 μm after 6 h) were obtained for the mechanically microflawed samples, revealing that chemical etchings (hot and cold strong acids, molten base or CF₄ plasma) are not crucial for good CVD diamond quality on Si₃N₄.     

Experimental and modelling study of gas dispersion in a double turbine stirred tank

Gas dispersion in a double turbine stirred tank is experimentally characterised by measuring local gas holdups and local bubble size distributions throughout the tank, for three liquid media: tap water, aqueous sulphate solution and aqueous sulphate solution with PEG. For all these media, bubble coalescence generally prevails over breakage. Where average bubble size decreases, this can be attributed to the difference in slip velocity between different sized bubbles. Most of the coalescence takes place in the turbine discharge stream.A compartment model that takes into account the combined effect of bubble coalescence and breakage is used to simulate gas dispersion. The model predicts spatial distribution of gas holdup and of average bubble size, with average bubble size at the turbines as an input. Reasonable agreement between experiment and simulation is achieved with optimisation of two parameters, one affecting mainly the slip velocity, the other related mainly to the bubble coalescence/breakage balance. Different sets of parameters are required for each of the three liquid systems under study, but are independent of stirring/aeration conditions. The model only fails to simulate the smaller average bubble diameters at the bottom of the tank.     

Competence management in knowledge intensive organizations using consensual knowledge and ontologies

This article describes an architecture suitable for use in a competence management system for knowledge intensive organizations (KIOs). The underlying motivation for this work is to explore the practical problems of the use of codified knowledge in knowledge management systems (KMS) in KIOs. We explore some of the key issues associated with the use of tacit and codified knowledge in KMS, and describe an architecture based on an ontology-driven framework derived from collective and consensual knowledge that acts as a structure for a formal knowledge base. We describe, in outline, a prototype competence management system based on this architecture designed to support the management of competencies in a structured way. We conclude with some observations about our approach to the representation of knowledge in a KMS and its potential value to KIOs.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.