Síntese, estudo cinético e análise microestrutural do sistema cério-níquel obtido pelo método pechini

The cerium oxide has a high potential for use in removing pollutants after combustion, removal of organic matter in waste water and the fuel-cell technology. The nickel oxide is an attractive material due to its excellent chemical stability and their optical properties, electrical and magnetic. In this work, CeO2-NiO- systems on molars reasons 1:1(I), 1:2(II) e 1:3(III) metal-citric acid were synthesized using the Pechini method. We used techniques of TG / DTG and ATD to monitor the degradation process of organic matter to the formation of the oxide. By thermogravimetric analysis and applying the dynamic method proposed by Coats-Redfern, it was possible to study the reactions of thermal decomposition in order to propose the possible mechanism by which the reaction takes place, as well as the determination of kinetic parameters as activation energy, Ea, pre-exponential factor and parameters of activation. It was observed that both variables exert a significant influence on the formation of complex polymeric precursor. The model that best fitted the experimental data in the dynamic mode was R3, which consists of nuclear growth, which formed the nuclei grow to a continuous reaction interface, it proposes a spherical symmetry (order 2 / 3). The values of enthalpy of activation of the system showed that the reaction in the state of transition is exothermic. The variables of composition, together with the variable temperature of calcination were studied by different techniques such as XRD, IV and SEM. Also a study was conducted microstructure by the Rietveld method, the calculation routine was developed to run the package program FullProf Suite, and analyzed by pseudo-Voigt function. It was found that the molar ratio of variable metal-citric acid in the system CeO2-NiO (I), (II), (III) has strong influence on the microstructural properties, size of crystallites and microstrain network, and can be used to control these properties

Desenvolvimento de um sistema Inteligente para a análise de cartas dinamométricas no método de elevação por bombeio mecânico

The artificial lifting of oil is needed when the pressure of the reservoir is not high enough so that the fluid contained in it can reach the surface spontaneously. Thus the increase in energy supplies artificial or additional fluid integral to the well to come to the surface. The rod pump is the artificial lift method most used in the world and the dynamometer card (surface and down-hole) is the best tool for the analysis of a well equipped with such method. A computational method using Artificial Neural Networks MLP was and developed using pre-established patterns, based on its geometry, the downhole card are used for training the network and then the network provides the knowledge for classification of new cards, allows the fails diagnose in the system and operation conditions of the lifting system. These routines could be integrated to a supervisory system that collects the cards to be analyzed

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)