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

Sistemas de medição de desempenho para projetos de PD&I no Setor de Petróleo e Gás Natural

Organizations are seeking new ideas, tools and methods aiming to improve management process and performance. On the other hand, system performance measurement needs to portray organizational changes and provide managers with a set of true and more appropriate information for the decision-making process. This work aims to propose a performance measurement system in the academic field regarding Research, Development and Innovation (RDI) in the oil and gas industry. The research performed a bibliographic review in a descriptive exploratory manner. A field research was conducted with an expert focus group in order to gather new indicators. As for the validation of these indicators, a survey with experienced professional was also realized. The research surveyed four segments in and outside of the Federal University of Rio Grande do Norte-Brazil such as oil and gas project coordinators, staff at Academic Planning Offices, FUNPEC employees as well as coordinators from Petrobrás. The performance measuring system created from this study features three interrelated performance indicators pointed out as: process indicators, outcome indicators and global indicators. The proposal includes performance indicators that seek to establish more appropriate strategies for effective institution management. It might help policy making of university-industry interaction policies

Software inteligente embarcado aplicado à correção de erro na medição de vazão em gás natural

This study developed software rotines, in a system made basically from a processor board producer of signs and supervisory, wich main function was correcting the information measured by a turbine gas meter. This correction is based on the use of an intelligent algorithm formed by an artificial neural net. The rotines were implemented in the habitat of the supervisory as well as in the habitat of the DSP and have three main itens: processing, communication and supervision

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

Sistemas de medição de desempenho para projetos de PD&I no Setor de Petróleo e Gás Natural

Organizations are seeking new ideas, tools and methods aiming to improve management process and performance. On the other hand, system performance measurement needs to portray organizational changes and provide managers with a set of true and more appropriate information for the decision-making process. This work aims to propose a performance measurement system in the academic field regarding Research, Development and Innovation (RDI) in the oil and gas industry. The research performed a bibliographic review in a descriptive exploratory manner. A field research was conducted with an expert focus group in order to gather new indicators. As for the validation of these indicators, a survey with experienced professional was also realized. The research surveyed four segments in and outside of the Federal University of Rio Grande do Norte-Brazil such as oil and gas project coordinators, staff at Academic Planning Offices, FUNPEC employees as well as coordinators from Petrobrás. The performance measuring system created from this study features three interrelated performance indicators pointed out as: process indicators, outcome indicators and global indicators. The proposal includes performance indicators that seek to establish more appropriate strategies for effective institution management. It might help policy making of university-industry interaction policies

Síntese de superfícies seletivas de frequência atráves de técnicas de computação natural

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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