Detecção e diagnostico de agarramento em válvulas posicionadoras

Valve stiction, or static friction, in control loops is a common problem in modern industrial processes. Recently, many studies have been developed to understand, reproduce and detect such problem, but quantification still remains a challenge. Since the valve position (mv) is normally unknown in an industrial process, the main challenge is to diagnose stiction knowing only the output signals of the process (pv) and the control signal (op). This paper presents an Artificial Neural Network approach in order to detect and quantify the amount of static friction using only the pv and op information. Different methods for preprocessing the training set of the neural network are presented. Those methods are based on the calculation of centroid and Fourier Transform. The proposal is validated using a simulated process and the results show a satisfactory measurement of stiction.

Análise de métodos para detecção de mudanças estruturais em séries temporais climáticas

The time series analysis has played an increasingly important role in weather and climate studies. The success of these studies depends crucially on the knowledge of the quality of climate data such as, for instance, air temperature and rainfall data. For this reason, one of the main challenges for the researchers in this field is to obtain homogeneous series. A time series of climate data is considered homogeneous when the values of the observed data can change only due to climatic factors, i.e., without any interference from external non-climatic factors. Such non-climatic factors may produce undesirable effects in the time series, as unrealistic homogeneity breaks, trends and jumps. In the present work it was investigated climatic time series for the city of Natal, RN, namely air temperature and rainfall time series, for the period spanning from 1961 to 2012. The main purpose was to carry out an analysis in order to check the occurrence of homogeneity breaks or trends in the series under investigation. To this purpose, it was applied some basic statistical procedures, such as normality and independence tests. The occurrence of trends was investigated by linear regression analysis, as well as by the Spearman and Mann-Kendall tests. The homogeneity was investigated by the SNHT, as well as by the Easterling-Peterson and Mann-Whitney-Pettit tests. Analyzes with respect to normality showed divergence in their results. The von Neumann ratio test showed that in the case of the air temperature series the data are not independent and identically distributed (iid), whereas for the rainfall series the data are iid. According to the applied testings, both series display trends. The mean air temperature series displays an increasing trend, whereas the rainfall series shows an decreasing trend. Finally, the homogeneity tests revealed that all series under investigations present inhomogeneities, although they breaks depend on the applied test. In summary, the results showed that the chosen techniques may be applied in order to verify how well the studied time series are characterized. Therefore, these results should be used as a guide for further investigations about the statistical climatology of Natal or even of any other place.

Desenvolvimento e validação de testes de fluxo lateral para a detecção de cultivares geneticamente modificados e de aflatoxinas em produtos agrícolas

The expansion of cultivated areas with genetically modified crops (GM) is a worldwide phenomenon, stimulating regulatory authorities to implement strict procedures to monitor and verify the presence of GM varieties in agricultural crops. With the constant growing of plant cultivating areas all over the world, consumption of aflatoxin-contaminated food also increased. Aflatoxins correspond to a class of highly toxic contaminants found in agricultural products that can have harmful effects on human and animal health. Therefore, the safety and quality evaluation of agricultural products are important issues for consumers. Lateral flow tests (strip tests) is a promising method for the detection both proteins expressed in GM crops and aflatoxins-contaminated food samples. The advantages of this technique include its simplicity, rapidity and cost-effective when compared to the conventional methods. In this study, two novel and sensitive strip tests assay were developed for the identification of: (i) Cry1Ac and Cry8Ka5 proteins expressed in GM cotton crops and; (ii) aflatoxins from agricultural products. The first strip test was developed using a sandwhich format, while the second one was developed using a competitive format. Gold colloidal nanoparticles were used as detector reagent when coated with monoclonal antibodies. An anti-species specific antibody was sprayed at the nitrocellulose membrane to be used as a control line. To validate the first strip test, GM (Bollgard I® e Planta 50- EMBRAPA) and non-GM cotton leaf (Cooker 312) were used. The results showed that the strip containing antibodies for the identification of Cry1Ac and Cry8Ka5 proteins was capable of correctly distinguishing between GM samples (positive result) and non-GM samples (negative result), in a high sensitivity manner. To validate the second strip test, artificially contaminated soybean with Aspergillus flavus (aflatoxin-producing fungus) was employed. Food samples, such as milk and soybean, were also evaluated for the presence of aflatoxins. The strip test was capable to distinguish between samples with and without aflatoxins samples, at a sensitivity concentration of 0,5 μg/Kg. Therefore, these results suggest that the strip tests developed in this study can be a potential tool as a rapid and cost-effective method for detection of insect resistant GM crops expressing Cry1Ac and Cry8Ka5 and aflatoxins from food samples.

Desenvolvimento e validação de testes de fluxo lateral para a detecção de cultivares geneticamente modificados e de aflatoxinas em produtos agrícolas

The expansion of cultivated areas with genetically modified crops (GM) is a worldwide phenomenon, stimulating regulatory authorities to implement strict procedures to monitor and verify the presence of GM varieties in agricultural crops. With the constant growing of plant cultivating areas all over the world, consumption of aflatoxin-contaminated food also increased. Aflatoxins correspond to a class of highly toxic contaminants found in agricultural products that can have harmful effects on human and animal health. Therefore, the safety and quality evaluation of agricultural products are important issues for consumers. Lateral flow tests (strip tests) is a promising method for the detection both proteins expressed in GM crops and aflatoxins-contaminated food samples. The advantages of this technique include its simplicity, rapidity and cost-effective when compared to the conventional methods. In this study, two novel and sensitive strip tests assay were developed for the identification of: (i) Cry1Ac and Cry8Ka5 proteins expressed in GM cotton crops and; (ii) aflatoxins from agricultural products. The first strip test was developed using a sandwhich format, while the second one was developed using a competitive format. Gold colloidal nanoparticles were used as detector reagent when coated with monoclonal antibodies. An anti-species specific antibody was sprayed at the nitrocellulose membrane to be used as a control line. To validate the first strip test, GM (Bollgard I® e Planta 50- EMBRAPA) and non-GM cotton leaf (Cooker 312) were used. The results showed that the strip containing antibodies for the identification of Cry1Ac and Cry8Ka5 proteins was capable of correctly distinguishing between GM samples (positive result) and non-GM samples (negative result), in a high sensitivity manner. To validate the second strip test, artificially contaminated soybean with Aspergillus flavus (aflatoxin-producing fungus) was employed. Food samples, such as milk and soybean, were also evaluated for the presence of aflatoxins. The strip test was capable to distinguish between samples with and without aflatoxins samples, at a sensitivity concentration of 0,5 μg/Kg. Therefore, these results suggest that the strip tests developed in this study can be a potential tool as a rapid and cost-effective method for detection of insect resistant GM crops expressing Cry1Ac and Cry8Ka5 and aflatoxins from food samples.

Estudo do genoma do vírus causador da mionecrose infecciosa em camarões e desenvolvimento de métodos para detecção de polimorfismos

Shrimp farming is one of the activities that contribute most to the growth of global aquaculture. However, this business has undergone significant economic losses due to the onset of viral diseases such as Infectious Myonecrosis (IMN). The IMN is already widespread throughout Northeastern Brazil and affects other countries such as Indonesia, Thailand and China. The main symptom of disease is myonecrosis, which consists of necrosis of striated muscles of the abdomen and cephalothorax of shrimp. The IMN is caused by infectious myonecrosis virus (IMNV), a non-enveloped virus which has protrusions along its capsid. The viral genome consists of a single molecule of double-stranded RNA and has two Open Reading Frames (ORFs). The ORF1 encodes the major capsid protein (MCP) and a potential RNA binding protein (RBP). ORF2 encodes a probable RNA-dependent RNA polymerase (RdRp) and classifies IMNV in Totiviridae family. Thus, the objective of this research was study the IMNV complete genome and encoded proteins in order to develop a system differentiate virus isolates based on polymorphisms presence. The phylogenetic relationship among some totivirus was investigated and showed a new group to IMNV within Totiviridae family. Two new genomes were sequenced, analyzed and compared to two other genomes already deposited in GenBank. The new genomes were more similar to each other than those already described. Conserved and variable regions of the genome were identified through similarity graphs and alignments using the four IMNV sequences. This analyze allowed mapping of polymorphic sites and revealed that the most variable region of the genome is in the first half of ORF1, which coincides with the regions that possibly encode the viral protrusion, while the most stable regions of the genome were found in conserved domains of proteins that interact with RNA. Moreover, secondary structures were predicted for all proteins using various softwares and protein structural models were calculated using threading and ab initio modeling approaches. From these analyses was possible to observe that the IMNV proteins have motifs and shapes similar to proteins of other totiviruses and new possible protein functions have been proposed. The genome and proteins study was essential for development of a PCR-based detection system able to discriminate the four IMNV isolates based on the presence of polymorphic sites

Estudo e implementação de algoritmos inteligentes para detecção e classificação de falhas na medição de gás natural

This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented

Implementação de um Módulo de Supervisão para um Sistema de Detecção de Vazamentos em Dutos de Petróleo

The transport of fluids through pipes is used in the oil industry, being the pipelines an important link in the logistics flow of fluids. However, the pipelines suffer deterioration in their walls caused by several factors which may cause loss of fluids to the environment, justifying the investment in techniques and methods of leak detection to minimize fluid loss and environmental damage. This work presents the development of a supervisory module in order to inform to the operator the leakage in the pipeline monitored in the shortest time possible, in order that the operator log procedure that entails the end of the leak. This module is a component of a system designed to detect leaks in oil pipelines using sonic technology, wavelets and neural networks. The plant used in the development and testing of the module presented here was the system of tanks of LAMP, and its LAN, as monitoring network. The proposal consists of, basically, two stages. Initially, assess the performance of the communication infrastructure of the supervisory module. Later, simulate leaks so that the DSP sends information to the supervisory performs the calculation of the location of leaks and indicate to which sensor the leak is closer, and using the system of tanks of LAMP, capture the pressure in the pipeline monitored by piezoresistive sensors, this information being processed by the DSP and sent to the supervisory to be presented to the user in real time

Detecção de interface em tanques de petróleo usando transmissores de nível e controladores Fuzzy

This work presents a proposal to detect interface in atmospheric oil tanks by installing a differential pressure level transmitter to infer the oil-water interface. The main goal of this project is to maximize the quantity of free water that is delivered to the drainage line by controlling the interface. A Fuzzy Controller has been implemented by using the interface transmitter as the Process Variable. Two ladder routine was generated to perform the control. One routine was developed to calculate the error and error variation. The other was generate to develop the fuzzy controller itself. By using rules, the fuzzy controller uses these variables to set the output. The output is the position variation of the drainage valve. Although the ladder routine was implemented into an Allen Bradley PLC, Control Logix family it can be implemented into any brand of PLCs

Sistema de sensoriamento eletromagnético utilizado para detecção da contaminação do óleo isolante do motor no método de elevação artificial do tipo bombeio centrífugo submerso

In the artificial lift method by Electrical Submersible Pump (ESP), the energy is transmitted for the well´s deep through a flat electric handle, where it is converted into mechanical energy through an engine of sub-surface, which is connected to a centrifugal pump. This transmits energy to the fluid under the pressure form, bringing it to the surface In this method the subsurface equipment is basically divided into: pump, seal and motor. The main function of the seal is the protect the motor, avoiding the motor´s oil be contaminated by oil production and the consequent burning of it. Over time, the seal will be wearing and initiates a contamination of motor oil, causing it to lose its insulating characteristics. This work presents a design of a magnetic sensor capable of detecting contamination of insulating oil used in the artificial lift method of oil-type Electrical Submersible Pump (ESP). The objective of this sensor is to generate alarm signal just the moment when the contamination in the isolated oil is present, enabling the implementation of a predictive maintenance. The prototype was designed to work in harsh conditions to reach a depth of 2000m and temperatures up to 150°C. It was used a simulator software to defined the mechanical and electromagnetic variables. Results of field experiments were performed to validate the prototype. The final results performed in an ESP system with a 62HP motor showed a good reliability and fast response of the prototype.