O papel de interações positivas entre plantas na regeneração de áreas degradadas na caatinga

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Estrutura ANFIS modificada para identificação e controle de plantas com ampla faixa de operação e não linearidade acentuada

In this work a modification on ANFIS (Adaptive Network Based Fuzzy Inference System) structure is proposed to find a systematic method for nonlinear plants, with large operational range, identification and control, using linear local systems: models and controllers. This method is based on multiple model approach. This way, linear local models are obtained and then those models are combined by the proposed neurofuzzy structure. A metric that allows a satisfactory combination of those models is obtained after the structure training. It results on plant s global identification. A controller is projected for each local model. The global control is obtained by mixing local controllers signals. This is done by the modified ANFIS. The modification on ANFIS architecture allows the two neurofuzzy structures knowledge sharing. So the same metric obtained to combine models can be used to combine controllers. Two cases study are used to validate the new ANFIS structure. The knowledge sharing is evaluated in the second case study. It shows that just one modified ANFIS structure is necessary to combine linear models to identify, a nonlinear plant, and combine linear controllers to control this plant. The proposed method allows the usage of any identification and control techniques for local models and local controllers obtaining. It also reduces the complexity of ANFIS usage for identification and control. This work has prioritized simpler techniques for the identification and control systems to simplify the use of the method

Sistema Híbrido de Inferência Baseado em Análise de Componentes Principais e Redes Neurais Artificiais Aplicado a Plantas de Processamento de Gás Natural

Nowadays, where the market competition requires products with better quality and a constant search for cost savings and a better use of raw materials, the research for more efficient control strategies becomes vital. In Natural Gas Processin Units (NGPUs), as in the most chemical processes, the quality control is accomplished through their products composition. However, the chemical composition analysis has a long measurement time, even when performed by instruments such as gas chromatographs. This fact hinders the development of control strategies to provide a better process yield. The natural gas processing is one of the most important activities in the petroleum industry. The main economic product of a NGPU is the liquefied petroleum gas (LPG). The LPG is ideally composed by propane and butane, however, in practice, its composition has some contaminants, such as ethane and pentane. In this work is proposed an inferential system using neural networks to estimate the ethane and pentane mole fractions in LPG and the propane mole fraction in residual gas. The goal is to provide the values of these estimated variables in every minute using a single multilayer neural network, making it possibly to apply inferential control techniques in order to monitor the LPG quality and to reduce the propane loss in the process. To develop this work a NGPU was simulated in HYSYS R software, composed by two distillation collumns: deethanizer and debutanizer. The inference is performed through the process variables of the PID controllers present in the instrumentation of these columns. To reduce the complexity of the inferential neural network is used the statistical technique of principal component analysis to decrease the number of network inputs, thus forming a hybrid inferential system. It is also proposed in this work a simple strategy to correct the inferential system in real-time, based on measurements of the chromatographs which may exist in process under study

Estudo e implementação em plantas físicas de um controlador preditivo generalizado com restrições

This work shows a study about the Generalized Predictive Controllers with Restrictions and their implementation in physical plants. Three types of restrictions will be discussed: restrictions in the variation rate of the signal control, restrictions in the amplitude of the signal control and restrictions in the amplitude of the Out signal (plant response). At the predictive control, the control law is obtained by the minimization of an objective function. To consider the restrictions, this minimization of the objective function is done by the use of a method to solve optimizing problems with restrictions. The chosen method was the Rosen Algorithm (based on the Gradient-projection). The physical plants in this study are two didactical systems of water level control. The first order one (a simple tank) and another of second order, which is formed by two tanks connected in cascade. The codes are implemented in C++ language and the communication with the system to be done through using a data acquisition panel offered by the system producer

Um sistema computacional para diagnosticar viroses de plantas usando a técnica de PCR com construção de primers espécie-específicos

It proposes a established computational solution in the development of a software to construct species-specific primers, used to improve the diagnosis of virus of plant for PCR. Primers are indispensable to PCR reaction, besides providing the specificity of the diagnosis. Primer is a synthetic, short, single stranded piece of DNA, used as a starter in PCR technique. It flanks the sequence desired to amplify. Species-specific primers indicate the well known region of beginning and ending where the polymerase enzyme is going to amplify on a certain species, i.e. it is specific for only a species. Thus, the main objective of this work is to automatize the process of choice of primers, optimizing the specificity of chosen primers by the traditional method

Características da carcaça e qualidade da carne de ovinos terminados em pastagens cultivadas

The objective of this study was to evaluate the quality of housing and the physical and chemical characteristics of meat from sheep raised on pasture Brachiaria brizantha and Panicum maximum. The experiment was conducted in the physical area of the Study Group on Forage (GEFOR), located in the Academic Unit Specialized in Agricultural Sciences - Federal University of Rio Grande do Norte - UFRN in Macaíba, RN, Brazil. We used 32 lambs SPRD, obtained from herds in the state, with liveweight (LW) of 24.5 kg were assigned randomly to four treatments consisting of tropical grasses, two cultivars of Brachiaria brizantha, Marandu and Piatã, and two of Panicum maximum, Aruana and Massai. The experimental area was 2.88 ha, divided into 4 paddocks of 0.72 ha, where each picket consisted of a farm and was divided into six plots of 0.12 ha, where the animals remained under rotational grazing. The period of adaptation to the pickets was seven days. At the beginning of the experiment the animals were weighed, identified with plastic earrings and necklaces colored according to the treatment, and treated against. The lambs were loose in the paddock at 8 am and collected at 16 hours, which returned to collective pens. During the time of grazing animals had free access to mineral supplement with monensin Ovinofós ® and water. Before entering the paddocks of pasture were sampled to characterize the chemical composition. Every seven days occurred at weighing, with fasting, to monitor the weight development. Cultivars Marandu, Aruana, Piatã and Massai were grazed for 133, 129, 143 and 142 days, respectively, until the lambs reach slaughter weight. Arriving at 32 kg lambs were evaluated subjectively for body condition score by, passed through fasting period, diet and water for 16 hours were slaughtered. Measurements were made in the inner and outer casings in addition to subjective evaluations regarding muscling, finish and quantity of pelvic-renal fat, then each was divided longitudinally into two half-carcases and cuts were made in the commercial left half, and after heavy calculated their income. Between the 12th and 13th thoracic vertebrae, was performed a cut to expose the cross section of the Longissimus dorsi, which was drawn on the rib eye area (REA) in transparent film. Fat thickness and extent of AOL GR were determined using a caliper. A tissue composition was determined by dissection of the legs. Analyzes were performed physical (color, cooking loss and shear force) and chemical composition of meat (moisture, ash, protein and lipids) in Longissimus dorsi muscle. Grazing tropical grass Brachiaria brizantha cvs. Marandu and Piatã and Panicum maximum cvs. Aruana and Massai can be used for lambs SRPD in the rainy season, because not alter the physico-chemical and chemical composition of meat

Métodos estatísticos recursivos aplicados ao problema de estimação de vazão de gás em plantas de Plunger-lift

This work has as main objective to find mathematical models based on linear parametric estimation techniques applied to the problem of calculating the grow of gas in oil wells. In particular we focus on achieving grow models applied to the case of wells that produce by plunger-lift technique on oil rigs, in which case, there are high peaks in the grow values that hinder their direct measurement by instruments. For this, we have developed estimators based on recursive least squares and make an analysis of statistical measures such as autocorrelation, cross-correlation, variogram and the cumulative periodogram, which are calculated recursively as data are obtained in real time from the plant in operation; the values obtained for these measures tell us how accurate the used model is and how it can be changed to better fit the measured values. The models have been tested in a pilot plant which emulates the process gas production in oil wells