961 resultados para Statistical process control


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O presente trabalho relata um estudo sobre a aplicação de duas metodologias diferentes do processo ensino-aprendizagem da disciplina Estatística no curso de Formação de Psicologia da Faculdade de Humanidades Pedro II. O estudo empírico realizou-se no primeiro semestre letivo de 1984. Utilizou-se um plano de pesquisa quase-experimental, com o modelo pré-teste/pós-teste. Formaram-se dois grupos intactos. O primeiro grupo constava de 64 sujeitos e o segundo de 66 sujeitos. Foram aplicadas, respectivamente, a metodologia Renovadora, que tinha por objetivo a aprendizagem através da compreensão e da aplicabilidade de conceitos e princípios estatísticos, e a metodologia Tradicional que implicava na aprendizagem por memorização dos princípios e automatização na execução dos cálculos. Os resultados do tratamento estatístico, considerando-se o nível de significância de 0,05, evidenciou não haver diferença estatisticamente significativa entre as duas metodologias de ensino da disciplina Estatística, tanto no que se refere à atitude quanto no que se refere ao aproveitamento.

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O propósito deste estudo é compreender como os diferentes membros gerenciam o risco na cadeia de suprimentos global. Por meio de um estudo multicaso na cadeia de exportação da manga brasileira para os Estados Unidos foram investigados os elos fornecedor, exportador, operador logístico e importador. Foi desenvolvido um protocolo de pesquisa conforme sugestão de Yin (2010) e adaptação de instrumento de coleta de dados semi-estruturado de Christopher, et al. (2011). A partir de entrevistas com organizações de apoio ao objeto da pesquisa, foi selecionado a amostra de empresas nas quais o fenômeno de gestão do risco poderia ser melhor observado. Baseado nas classificações de tipo de risco elaboradas por Christopher et al. (2011), é sugerida uma nova organização das categorias de risco (demanda, suprimentos, processo e controle, ambiental e sustentabilidade). Este trabalho apresenta uma descrição da cadeia de exportação de manga, bem como os principais riscos e as estratégias mitigadoras de risco empregadas pelos quatro membros da cadeia estudados. Conforme regra de Sousa (2001), foram realizadas comparações entre os tipos de risco e estratégias mitigadoras observadas na cadeia de suprimentos. Por fim, o estudo mostrou que a gestão do risco é heterogenia entre os membros da cadeia, o exportador é o mais penalizado pela consequência de risco à cadeia e a colaboração é a principal forma observada de mitigação.

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PEREIRA, J. P. ; CASTRO, B. P. S. ; VALENTIM, R. A. M. . Kit Educacional para Controle e Supervisão Aplicado a Nível. Holos, Natal, v. 2, p. 68-72, 2009

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The financial crisis that occurred between the years 2007 and 2008, known as the subprime crisis, has highlighted the governance of companies in Brazil and worldwide. To monitor the financial risk, quantitative tools of risk management were created in the 1990s, after several financial disasters. The market turmoil has also led companies to invest in the development and use of information, which are applied as tools to support process control and decision making. Numerous empirical studies on informational efficiency of the market have been made inside and outside Brazil, revealing whether the prices reflect the information available instantly. The creation of different levels of corporate governance on BOVESPA, in 2000, made the firms had greater impairment in relation to its shareholders with greater transparency in their information. The purpose of this study is to analyze how the subprime financial crisis has affected, between January 2007 and December 2009, the volatility of stock returns in the BM&BOVESPA of companies with greater liquidity at different levels of corporate governance. From studies of time series and through the studies of events, econometric tests were performed by the EVIEWS, and through the results obtained it became evident that the adoption of good practices of corporate governance affect the volatility of returns of companies

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Titanium nitride films were grown on glass using the Cathodic Cage Plasma Deposition technique in order to verify the influence of process parameters in optical and structural properties of the films. The plasma atmosphere used was a mixture of Ar, N2 and H2, setting the Ar and N2 gas flows at 4 and 3 sccm, respectively and H2 gas flow varied from 0, 1 to 2 sccm. The deposition process was monitored by Optical Emission Spectroscopy (OES) to investigate the influence of the active species in plasma. It was observed that increasing the H2 gas flow into the plasma the luminescent intensities associated to the species changed. In this case, the luminescence of N2 (391,4nm) species was not proportional to the increasing of the H2 gas into the reactor. Other parameters investigated were diameter and number of holes in the cage. The analysis by Grazing Incidence X-Ray Diffraction (GIXRD) confirmed that the obtained films are composed by TiN and they may have variations in the nitrogen amount into the crystal and in the crystallite size. The optical microscopy images provided information about the homogeneity of the films. The atomic force microscopy (AFM) results revealed some microstructural characteristics and surface roughness. The thickness was measured by ellipsometry. The optical properties such as transmittance and reflectance (they were measured by spectrophotometry) are very sensitive to changes in the crystal lattice of the material, chemical composition and film thicknesses. Therefore, such properties are appropriate tools for verification of this process control. In general, films obtained at 0 sccm of H2 gas flow present a higher transmittance. It can be attributed to the smaller crystalline size due to a higher amount of nitrogen in the TiN lattice. The films obtained at 1 and 2 sccm of H2 gas flow have a golden appearance and XRD pattern showed peaks characteristics of TiN with higher intensity and smaller FWHM (Full Width at Half Maximum) parameter. It suggests that the hydrogen presence in the plasma makes the films more stoichiometric and becomes it more crystalline. It was observed that with higher number of holes in the lid of the cage, close to the region between the lid and the sample and the smaller diameter of the hole, the deposited film is thicker, which is justified by the most probability of plasma species reach effectively the sample and it promotes the growth of the film

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In the present study we elaborated algorithms by using concepts from percolation theory which analyze the connectivity conditions in geological models of petroleum reservoirs. From the petrophysical parameters such as permeability, porosity, transmittivity and others, which may be generated by any statistical process, it is possible to determine the portion of the model with more connected cells, what the interconnected wells are, and the critical path between injector and source wells. This allows to classify the reservoir according to the modeled petrophysical parameters. This also make it possible to determine the percentage of the reservoir to which each well is connected. Generally, the connected regions and the respective minima and/or maxima in the occurrence of the petrophysical parameters studied constitute a good manner to characterize a reservoir volumetrically. Therefore, the algorithms allow to optimize the positioning of wells, offering a preview of the general conditions of the given model s connectivity. The intent is not to evaluate geological models, but to show how to interpret the deposits, how their petrophysical characteristics are spatially distributed, and how the connections between the several parts of the system are resolved, showing their critical paths and backbones. The execution of these algorithms allows us to know the properties of the model s connectivity before the work on reservoir flux simulation is started

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The petrochemical industry has as objective obtain, from crude oil, some products with a higher commercial value and a bigger industrial utility for energy purposes. These industrial processes are complex, commonly operating with large production volume and in restricted operation conditions. The operation control in optimized and stable conditions is important to keep obtained products quality and the industrial plant safety. Currently, industrial network has been attained evidence when there is a need to make the process control in a distributed way. The Foundation Fieldbus protocol for industrial network, for its interoperability feature and its user interface organized in simple configuration blocks, has great notoriety among industrial automation network group. This present work puts together some benefits brought by industrial network technology to petrochemical industrial processes inherent complexity. For this, a dynamic reconfiguration system for intelligent strategies (artificial neural networks, for example) based on the protocol user application layer is proposed which might allow different applications use in a particular process, without operators intervention and with necessary guarantees for the proper plant functioning

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The industrial automation is directly linked to the development of information tecnology. Better hardware solutions, as well as improvements in software development methodologies make possible the rapid growth of the productive process control. In this thesis, we propose an architecture that will allow the joining of two technologies in hardware (industrial network) and software field (multiagent systems). The objective of this proposal is to join those technologies in a multiagent architecture to allow control strategies implementations in to field devices. With this, we intend develop an agents architecture to detect and solve problems which may occur in the industrial network environment. Our work ally machine learning with industrial context, become proposed multiagent architecture adaptable to unfamiliar or unexpected production environment. We used neural networks and presented an allocation strategies of these networks in industrial network field devices. With this we intend to improve decision support at plant level and allow operations human intervention independent

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This work proposes the specification of a new function block according to Foundation Fieldbus standards. The new block implements an artificial neural network, which may be useful in process control applications. The specification includes the definition of a main algorithm, that implements a neural network, as well as the description of some accessory functions, which provide safety characteristics to the block operation. Besides, it also describes the block attributes emphasizing its parameters, which constitute the block interfaces. Some experimental results, obtained from an artificial neural network implementation using actual standard functional blocks on a laboratorial FF network, are also shown, in order to demonstrate the possibility and also the convenience of integrating a neural network to Fieldbus devices

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This paper describes the design, implementation and enforcement of a system for industrial process control based on fuzzy logic and developed using Java, with support for industrial communication protocol through the OPC (Ole for Process Control). Besides the java framework, the software is completely independent from other platforms. It provides friendly and functional tools for modeling, construction and editing of complex fuzzy inference systems, and uses these logical systems in control of a wide variety of industrial processes. The main requirements of the developed system should be flexibility, robustness, reliability and ease of expansion

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This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol

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This work proposes the design, the performance evaluation and a methodology for tuning the initial MFs parameters of output of a function based Takagi-Sugeno-Kang Fuzzy-PI controller to neutralize the pH in a stirred-tank reactor. The controller is designed to perform pH neutralization of industrial plants, mainly in units found in oil refineries where it is strongly required to mitigate uncertainties and nonlinearities. In addition, it adjusts the changes in pH regulating process, avoiding or reducing the need for retuning to maintain the desired performance. Based on the Hammerstein model, the system emulates a real plant that fits the changes in pH neutralization process of avoiding or reducing the need to retune. The controller performance is evaluated by overshoots, stabilization times, indices Integral of the Absolute Error (IAE) and Integral of the Absolute Value of the Error-weighted Time (ITAE), and using a metric developed by that takes into account both the error information and the control signal. The Fuzzy-PI controller is compared with PI and gain schedule PI controllers previously used in the testing plant, whose results can be found in the literature.

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The control, automation and optimization areas help to improve the processes used by industry. They contribute to a fast production line, improving the products quality and reducing the manufacturing costs. Didatic plants are good tools for research in these areas, providing a direct contact with some industrial equipaments. Given these capabilities, the main goal of this work is to model and control a didactic plant, which is a level and flow process control system with an industrial instrumentation. With a model it is possible to build a simulator for the plant that allows studies about its behaviour, without any of the real processes operational costs, like experiments with controllers. They can be tested several times before its application in a real process. Among the several types of controllers, it was used adaptive controllers, mainly the Direct Self-Tuning Regulators (DSTR) with Integral Action and the Gain Scheduling (GS). The DSTR was based on Pole-Placement design and use the Recursive Least Square to calculate the controller parameters. The characteristics of an adaptive system was very worth to guarantee a good performance when the controller was applied to the plant

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The control of industrial processes has become increasingly complex due to variety of factory devices, quality requirement and market competition. Such complexity requires a large amount of data to be treated by the three levels of process control: field devices, control systems and management softwares. To use data effectively in each one of these levels is extremely important to industry. Many of today s industrial computer systems consist of distributed software systems written in a wide variety of programming languages and developed for specific platforms, so, even more companies apply a significant investment to maintain or even re-write their systems for different platforms. Furthermore, it is rare that a software system works in complete isolation. In industrial automation is common that, software had to interact with other systems on different machines and even written in different languages. Thus, interoperability is not just a long-term challenge, but also a current context requirement of industrial software production. This work aims to propose a middleware solution for communication over web service and presents an user case applying the solution developed to an integrated system for industrial data capture , allowing such data to be available simplified and platformindependent across the network

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This work considers the development of a filtering system composed of an intelligent algorithm, that separates information and noise coming from sensors interconnected by Foundation Fieldbus (FF) network. The algorithm implementation will be made through FF standard function blocks, with on-line training through OPC (OLE for Process Control), and embedded technology in a DSP (Digital Signal Processor) that interacts with the fieldbus devices. The technique ICA (Independent Component Analysis), that explores the possibility of separating mixed signals based on the fact that they are statistically independent, was chosen to this Blind Source Separation (BSS) process. The algorithm and its implementations will be Presented, as well as the results