77 resultados para Process control -- Data processing


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Supercritical fluid extraction (SFE) from solids has proven to be technically feasible for almost any system; nonetheless, its economical viability has been proven for a restricted number of systems. A common practice is to compare the cost of manufacturing of vegetable extracts by a variety of techniques without deeply considering the huge differences in composition and functional properties among the various types of extracts obtained; under this circumstance, the cost of manufacturing do not favor SFE. Additionally, the influence of external parameters such as the agronomic conditions and the SFE system geometry are not considered. In the present work, these factors were studied for the system fennel seeds + CO2. The effects of the harvesting season and the degree of maturation on the global yields for the system fennel seeds + CO2 were analyzed at 300 bar and 40 degrees C. The effects of the pressure on the global yields were determined for the temperatures of 30 and 40 degrees C. Kinetics experiments were done for various ratios of bed height to bed diameter. Fennel extracts were also obtained by hydrodistillation and low-pressure solvent extraction. The chemical composition of the fennel extracts were determined by gas chromatography. The SFE maximum global yield (12.5%, dry basis) was obtained with dry harvested fennel seeds. Anethole and fenchone were the major constituents of the extract; the following fat acids palmitic (C16H32O2), palmitoleic stearic (C18H36O2), oleic (C18H34O2), linoleic (C18H32O2) and linolenic (C18H30O2) were also detected in the extracts. A relation between amounts of feed and solvent, bed height and diameter, and solvent flow rate was proposed. The models of Sovova, Goto et al. and Tan and Lion were capable of describing the mass transfer kinetics. (c) 2005 Elsevier B.V. All rights reserved.

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A portable or field test method for simultaneous spectrophotometric determination of calcium and magnesium in water using multivariate partial least squares (PLS) calibration methods is proposed. The method is based on the reaction between the analytes and methylthymol blue at pH 11. The spectral information was used as the X-block, and the Ca(II) and Mg(II) concentrations obtained by a reference technique (ICP-AES) were used as the Y-block. Two series of analyses were performed, with a month's difference between them. The first series was used as the calibration set and the second one as the validation set. Multivariate statistical process control (MSPC) techniques, based on statistics from principal component models, were used to study the features and evolution with time of the spectral signals. Signal standardization was used to correct the deviations between series. Method validation was performed by comparing the predictions of the PLS model with the reference Ca(II) and Mg(II) concentrations determined by ICP-AES using the joint interval test for the slope and intercept of the regression line with errors in both axes. (C) 1998 John Wiley & Sons, Ltd.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The soy-yogurt was used as food vehicle due to its therapeutic and nutritionalproperties and lower cost. The aim of this work was to develop an enriched soy-yogurt with 12 mg of elementary iron/1, with suitable sensory and technological properties. Four iron sources were tested: FeSO 4.7H 2O, NaFeEDTA, Ferrochel® and microencapsulated FeSO 4.7H 2O. The products were evaluated by fermentation time, pH, titratable acidity, viscosity, consistency, iron concentration and sensory properties (difference from the control and acceptance tests). Viscosity and consistency data were submitted to analysis of variance and Tukey's test. Difference from the control data was evaluated by analysis of variance and Dunnett's test and the acceptance test was evaluated by analysis of variance and Tukey's test. For all iron salts used in the enrichment process, only the FeSO 4JH 2O did not work out because of the undesirable sensorial characteristics of the final products. The others sources used in the enrichment process (NaFeEDTA, Ferrochel® and microencapsulated FeSO 4.7H 2O) did not alter the fermentation time, titratable acidity and sensory and reologics properties of the soy-yogurt.

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This paper adresses the problem on processing biological data such as cardiac beats, audio and ultrasonic range, calculating wavelet coefficients in real time, with processor clock running at frequency of present ASIC's and FPGA. The Paralell Filter Architecture for DWT has been improved, calculating wavelet coefficients in real time with hardware reduced to 60%. The new architecture, which also processes IDWT, is implemented with the Radix-2 or the Booth-Wallace Constant multipliers. Including series memory register banks, one integrated circuit Signal Analyzer, ultrasonic range, is presented.

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Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

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In this article, we evaluate the performance of the T2 chart based on the principal components (PC chart) and the simultaneous univariate control charts based on the original variables (SU X̄ charts) or based on the principal components (SUPC charts). The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU X̄, the SUPC and the T 2 charts we conclude that the SU X̄ charts (SUPC charts) have a better overall performance when the variables are positively (negatively) correlated. We also develop the expression to obtain the power of two S 2 charts designed for monitoring the covariance matrix. These joint S2 charts are, in the majority of the cases, more efficient than the generalized variance |S| chart.

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GPS active networks are more and more used in geodetic surveying and scientific experiments, as water vapor monitoring in the atmosphere and lithosphere plate movement. Among the methods of GPS positioning, Precise Point Positioning (PPP) has provided very good results. A characteristic of PPP is related to the modeling and / or estimation of the errors involved in this method. The accuracy obtained for the coordinates can reach few millimeters. Seasonal effects can affect such accuracy if they are not consistent treated during the data processing. Coordinates time series analyses have been realized using Fourier or Harmonics spectral analyses, wavelets, least squares estimation among others. An approach is presented in this paper aiming to investigate the seasonal effects included in the stations coordinates time series. Experiments were carried out using data from stations Manaus (NAUS) and Fortaleza (BRFT) which belong to the Brazilian Continuous GPS Network (RBMC). The coordinates of these stations were estimated daily using PPP and were analyzed through wavelets for identification of the periods of the seasonal effects (annual and semi-annual) in each time series. These effects were removed by means of a filtering process applied in the series via the least squares adjustment (LSQ) of a periodic function. The results showed that the combination of these two mathematical tools, wavelets and LSQ, is an interesting and efficient technique for removal of seasonal effects in time series.

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The rule creation to clone selection in different projects is a hard task to perform by using traditional implementations to control all the processes of the system. The use of an algebraic language is an alternative approach to manage all of system flow in a flexible way. In order to increase the power of versatility and consistency in defining the rules for optimal clone selection, this paper presents the software OCI 2 in which uses process algebra in the flow behavior of the system. OCI 2, controlled by an algebraic approach was applied in the rules elaboration for clone selection containing unique genes in the partial genome of the bacterium Bradyrhizobium elkanii Semia 587 and in the whole genome of the bacterium Xanthomonas axonopodis pv. citri. Copyright© (2009) by the International Society for Research in Science and Technology.

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In geophysics and seismology, raw data need to be processed to generate useful information that can be turned into knowledge by researchers. The number of sensors that are acquiring raw data is increasing rapidly. Without good data management systems, more time can be spent in querying and preparing datasets for analyses than in acquiring raw data. Also, a lot of good quality data acquired at great effort can be lost forever if they are not correctly stored. Local and international cooperation will probably be reduced, and a lot of data will never become scientific knowledge. For this reason, the Seismological Laboratory of the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of São Paulo (IAG-USP) has concentrated fully on its data management system. This report describes the efforts of the IAG-USP to set up a seismology data management system to facilitate local and international cooperation. © 2011 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved.

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Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

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Digital data sets constitute rich sources of information, which can be extracted and evaluated applying computational tools, for example, those ones for Information Visualization. Web-based applications, such as social network environments, forums and virtual environments for Distance Learning, are good examples for such sources. The great amount of data has direct impact on processing and analysis tasks. This paper presents the computational tool Mapper, defined and implemented to use visual representations - maps, graphics and diagrams - for supporting the decision making process by analyzing data stored in Virtual Learning Environment TelEduc-Unesp. © 2012 IEEE.

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Methylmercury (MeHg) is present in the environment because of natural and anthropogenic causes. MeHg can reach the central nervous system (CNS) and cause neurological damage in humans and animals. Electric organ discharges (EODs) in the weak electric fish Gymnotus sylvius are produced by the electric organ and modulated by the CNS. These discharges are used for electrolocation and communication. The purpose of the present study was to investigate the effects of dietary MeHg exposure on EOD rate in G. sylvius. An oscilloscope was used to record the EOD rate. Two treatments were investigated: chronic MeHg administration (4 μg/kg MeHg every 2 days, with a total of nine dietary exposures to MeHg) and acute MeHg administration (a single dose of 20 μg/kg MeHg). The control data for both treatments were collected every 2 days for 18 days, with a total of nine sessions (day 1 until day 18). Data of fish exposed to MeHg were collected every 2 days, totaling nine sessions (day 19 until day 36). Chronic treatment significantly increased the EOD rate in G. sylvius (p<.05), especially with the final treatment (day 32 until day 36). Acute treatment resulted in an initial increase in the EOD rate, which was maintained midway through the experiment (day 26 until day 30 p<.05). The present study provides the first insights into the effects of MeHg on EODs in weak electric fish. The EOD rate is a novel response of the fish to MeHg administration.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)