Inibição à corrosão do aço AISI 1020, em meios ácido e salino, por tensoativos e substâncias nitrogenadas microemulsionados

Corrosion inhibition efficiency of saponified coconut oil (SCO) and sodium dodecilbenzene sulfonate (DBS) surfactants in AISI 1020 carbon steel was evaluated by electrochemical methods. These surfactants were also evaluated as microemulsion systems (SCO-ME and DBS-ME), of O/W type (water-rich microemulsion), in a Winsor IV region. They were obtained according to the following composition: 15% SCO, 15% butanol (30% Co-surfactant/Surfactant C/T), 10% organic phase (FO, kerosene) and 60% aqueous phase (FA). These systems were also used to solubilize the following nitrogenated substances: Diphenylcarbazide (DC), 2,4-dinitro-phenyl-thiosemicarbazide (TSC) and the mesoionic type compound 1,3,4-triazolium-2-thiolate (MI), that were investigated with the purpose of evaluating their anticorrosive effects. Comparative studies of carbon steel corrosion inhibition efficiencies of free DBS and DBS-ME, in brine and acidic media (0.5%), showed that DBS presents better inhibition results in acidic media (free DBS, 89% and DBS-ME, 93%). However, the values obtained for DBS in salted solution (72% free DBS and 77% DBS-ME) were similar to the ones observed for the SCO surfactant in brine (63% free SCO and 74% SCO-ME). Analysis of corrosion inhibition of the nitrogenated substances that were solubilized in the SCO-ME microemulsion system by the linear polarization method in brine (0.5% NaCl) showed that such compounds are very efficient an corrosion inhibitors [DC-ME-SCO (92%), TSC-ME-SCO (93%) and MI-ME-SCO (94%)]

Modelagem linear e identificação do modelo dinâmico de um robô móvel com acionamento diferencial

This work presents a modelling and identification method for a wheeled mobile robot, including the actuator dynamics. Instead of the classic modelling approach, where the robot position coordinates (x,y) are utilized as state variables (resulting in a non linear model), the proposed discrete model is based on the travelled distance increment Delta_l. Thus, the resulting model is linear and time invariant and it can be identified through classical methods such as Recursive Least Mean Squares. This approach has a problem: Delta_l can not be directly measured. In this paper, this problem is solved using an estimate of Delta_l based on a second order polynomial approximation. Experimental data were colected and the proposed method was used to identify the model of a real robot

Implementação da rotina de unfolding para determinação de distribuição de tamanho de grãos esféricos via distribuição de interceptos lineares e de área de seção

Na unfolding method of linear intercept distributions and secction área distribution was implemented for structures with spherical grains. Although the unfolding routine depends on the grain shape, structures with spheroidal grains can also be treated by this routine. Grains of non-spheroidal shape can be treated only as approximation. A software was developed with two parts. The first part calculates the probability matrix. The second part uses this matrix and minimizes the chi-square. The results are presented with any number of size classes as required. The probability matrix was determined by means of the linear intercept and section area distributions created by computer simulation. Using curve fittings the probability matrix for spheres of any sizes could be determined. Two kinds of tests were carried out to prove the efficiency of the Technique. The theoretical tests represent ideal cases. The software was able to exactly find the proposed grain size distribution. In the second test, a structure was simulated in computer and images of its slices were used to produce the corresponding linear intercept the section area distributions. These distributions were then unfolded. This test simulates better reality. The results show deviations from the real size distribution. This deviations are caused by statistic fluctuation. The unfolding of the linear intercept distribution works perfectly, but the unfolding of section area distribution does not work due to a failure in the chi-square minimization. The minimization method uses a matrix inversion routine. The matrix generated by this procedure cannot be inverted. Other minimization method must be used

Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities

Análise não-linear de treliças pelo método dos elementos finitos posicional

This work presents the positional nonlinear geometric formulation for trusses using different strain measures. The positional formulation presents an alternative approach for nonlinear problems. This formulation considers nodal positions as variables of the nonlinear system instead of displacements (widely found in literature). The work also describes the arc-length method used for tracing equilibrium paths with snap-through and snap-back. Numerical applications for trusses already established in the literature and comparisons with other studies are provided to prove the accuracy of the proposed formulation

Controle preditivo não linear baseado no modelo de Hammerstein com prova de estabilidade

The Predictive Controller has been receiving plenty attention in the last decades, because the need to understand, to analyze, to predict and to control real systems has been quickly growing with the technological and industrial progress. The objective of this thesis is to present a contribution for the development and implementation of Nonlinear Predictive Controllers based on Hammerstein model, as well as to its make properties evaluation. In this case, in the Nonlinear Predictive Controller development the time-step linearization method is used and a compensation term is introduced in order to improve the controller performance. The main motivation of this thesis is the study and stability guarantee for the Nonlinear Predictive Controller based on Hammerstein model. In this case, was used the concepts of sections and Popov Theorem. Simulation results with literature models shows that the proposed approaches are able to control with good performance and to guarantee the systems stability

Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo

Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC

Otimização topológica de estruturas termoelásticas tridimensionais

This work presents an optimization technique based on structural topology optimization methods, TOM, designed to solve problems of thermoelasticity 3D. The presented approach is based on the adjoint method of sensitivity analysis unified design and is intended to loosely coupled thermomechanical problems. The technique makes use of analytical expressions of sensitivities, enabling a reduction in the computational cost through the use of a coupled field adjoint equation, defined in terms the of temperature and displacement fields. The TOM used is based on the material aproach. Thus, to make the domain is composed of a continuous distribution of material, enabling the use of classical models in nonlinear programming optimization problem, the microstructure is considered as a porous medium and its constitutive equation is a function only of the homogenized relative density of the material. In this approach, the actual properties of materials with intermediate densities are penalized based on an artificial microstructure model based on the SIMP (Solid Isotropic Material with Penalty). To circumvent problems chessboard and reduce dependence on layout in relation to the final optimal initial mesh, caused by problems of numerical instability, restrictions on components of the gradient of relative densities were applied. The optimization problem is solved by applying the augmented Lagrangian method, the solution being obtained by applying the finite element method of Galerkin, the process of approximation using the finite element Tetra4. This element has the ability to interpolate both the relative density and the displacement components and temperature. As for the definition of the problem, the heat load is assumed in steady state, i.e., the effects of conduction and convection of heat does not vary with time. The mechanical load is assumed static and distributed