Avaliação da cobertura e monitoramento do branqueamento de corais nos

Coral bleaching has been increasingly the focus of research around the world since the early 1980s, when it was verified to be increasing in frequency, intensity and amount of areas affected. The phenomenon has been recorded since 1993, associated with elevation of the sea surface temperature due to El Niños and water thermal anomalies, according to most reports around the world. On the coast of Rio Grande do Norte, Brazil, a mass coral bleaching event was recorded in the Environmental Protection Area of Coral Reefs (APARC) during March and April 2010, when the water temperature reached 34°C for several days. About 80% of the corals in Maracajaú reef-complex exhibited partial or total bleaching. The aims of this study were to verify representativeness of coral coverage and how the bleaching dynamic has developed among different species. Coral coverage was estimated according to Reef Check Brazil protocol associated with quadrant method, and bleaching was evaluated from biweekly visual surveys in 80 colonies of Favia gravida, Porites astreoides, Siderastrea stellata and Millepora alcicornis. At the same time temperature, pH, salinity and horizontal transparency, as well as mortality and disease occurrence were monitored. Analysis of variance and Multiple Regression from the perspective of time lag concept were used to evaluate the bleaching dynamics among species and the relationship between variation of means of bleaching and variations of abiotic parameters, respectively. Species showed significant differences among themselves as to variation of means of bleaching over time, but the dynamic of variation exhibited similar patterns

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