Técnicas de inteligência artificial para a geração dinâmica de set points para uma coluna de destilação

Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations

Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

Algoritmos inteligentes para auto-correção, auto-verificação e auto-validação de medidas em sensores

This work presents a set of intelligent algorithms with the purpose of correcting calibration errors in sensors and reducting the periodicity of their calibrations. Such algorithms were designed using Artificial Neural Networks due to its great capacity of learning, adaptation and function approximation. Two approaches willbe shown, the firstone uses Multilayer Perceptron Networks to approximate the many shapes of the calibration curve of a sensor which discalibrates in different time points. This approach requires the knowledge of the sensor s functioning time, but this information is not always available. To overcome this need, another approach using Recurrent Neural Networks was proposed. The Recurrent Neural Networks have a great capacity of learning the dynamics of a system to which it was trained, so they can learn the dynamics of a sensor s discalibration. Knowingthe sensor s functioning time or its discalibration dynamics, it is possible to determine how much a sensor is discalibrated and correct its measured value, providing then, a more exact measurement. The algorithms proposed in this work can be implemented in a Foundation Fieldbus industrial network environment, which has a good capacity of device programming through its function blocks, making it possible to have them applied to the measurement process

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Análise de antenas utilizando substrato cerâmico, zpt, produzido por síntese auto propagante para aplicações em sistemas de micro-ondas

Ceramic substrates have been investigated by researchers around the world and has achieved a high interest in the scientific community, because they had high dielectric constants and excellent performance in the structures employed. Such ceramics result in miniaturized structures with dimensions well reduced and high radiation efficiency. In this work, we have used a new ceramic material called lead zinc titanate in the form of Zn0,8Pb0,2TiO3, capable of being used as a dielectric substrate in the construction of various structures of antennas. The method used in constructing the ceramic combustion synthesis was Self- Sustained High Temperature (SHS - "Self-Propagating High-Temperature Synthesis") which is defined as a process that uses highly exothermic reactions to produce various materials. Once initiated the reaction area in the reaction mixture, the heat generated is sufficient to become self-sustaining combustion in the form of a wave that propagates converting the reaction mixture into the product of interest. Were analyzed aspects of the formation of the composite Zn0,8Pb0,2TiO3 by SHS powders and characterized. The analysis consisted of determining the parameters of the reaction for the formation of the composite, as the ignition temperature and reaction mechanisms. The production of composite Zn0,8Pb0,2TiO3 by SHS performed in the laboratory, was the result of a total control of combustion temperature and after obtaining the powder began the development of ceramics. The product was obtained in the form of regular, alternating layers of porous ceramics and was obtained by uniaxial pressing. 10 The product was characterized by analysis of dilatometry, X-ray diffraction analysis and scanning electron microscopy. One of the contributions typically defined in this work is the development of a new dielectric material, nevertheless presented previously in the literature. Therefore, the structures of the antennas presented in this work consisted of new dielectric ceramics based Zn0,8Pb0,2TiO3 usually used as dielectric substrate. The materials produced were characterized in the microwave range. These are dielectrics with high relative permittivity and low loss tangent. The Ansoft HFSS, commercial program employee, using the finite element method, and was used for analysis of antennas studied in this work

Análise de desempenho de sistemas distribuídos de grande porte na plataforma Java

The lava Platform is increasing1y being adopted in the development of distributed sys¬tems with higb user demando This kind of application is more complex because it needs beyond attending the functional requirements, to fulfil1 the pre-established performance parameters. This work makes a study on the Java Vutual Machine (JVM), approaching its intemal aspects and exploring the garbage collection strategies existing in the literature and used by the NM. It also presents a set of tools that helps in the job of optimizing applications and others that help in the monitoring of applications in the production envi¬ronment. Doe to the great amount of technologies that aim to solve problems which are common to the application layer, it becomes difficult to choose the one with best time response and less memory usage. This work presents a brief introduction to each one of tbe possible technologies and realize comparative tests through a statistical analysis of the response time and garbage collection activity random variables. The obtained results supply engineers and managers with a subside to decide which technologies to use in large applications through the knowledge of how they behave in their environments and the amount of resources that they consume. The relation between the productivity of the technology and its performance is also considered ao important factor in this choice

Otimização de superfícies seletivas de frequência com elementos pré-fractais utilizando rede neural MLP e algoritmos de busca populacional

This thesis describes design methodologies for frequency selective surfaces (FSSs) composed of periodic arrays of pre-fractals metallic patches on single-layer dielectrics (FR4, RT/duroid). Shapes presented by Sierpinski island and T fractal geometries are exploited to the simple design of efficient band-stop spatial filters with applications in the range of microwaves. Initial results are discussed in terms of the electromagnetic effect resulting from the variation of parameters such as, fractal iteration number (or fractal level), fractal iteration factor, and periodicity of FSS, depending on the used pre-fractal element (Sierpinski island or T fractal). The transmission properties of these proposed periodic arrays are investigated through simulations performed by Ansoft DesignerTM and Ansoft HFSSTM commercial softwares that run full-wave methods. To validate the employed methodology, FSS prototypes are selected for fabrication and measurement. The obtained results point to interesting features for FSS spatial filters: compactness, with high values of frequency compression factor; as well as stable frequency responses at oblique incidence of plane waves. This thesis also approaches, as it main focus, the application of an alternative electromagnetic (EM) optimization technique for analysis and synthesis of FSSs with fractal motifs. In application examples of this technique, Vicsek and Sierpinski pre-fractal elements are used in the optimal design of FSS structures. Based on computational intelligence tools, the proposed technique overcomes the high computational cost associated to the full-wave parametric analyzes. To this end, fast and accurate multilayer perceptron (MLP) neural network models are developed using different parameters as design input variables. These neural network models aim to calculate the cost function in the iterations of population-based search algorithms. Continuous genetic algorithm (GA), particle swarm optimization (PSO), and bees algorithm (BA) are used for FSSs optimization with specific resonant frequency and bandwidth. The performance of these algorithms is compared in terms of computational cost and numerical convergence. Consistent results can be verified by the excellent agreement obtained between simulations and measurements related to FSS prototypes built with a given fractal iteration

Aplicação de superfícies seletivas em frequência para melhoria de resposta de arranjos de antenas planares

This work aims to show how the application of frequency selective surfaces (FSS) in planar antenna arrays become an alternative to obtain desired radiation characteristics from changes in radiation parameters of the arrays, such as bandwidth, gain and directivity. In addition to analyzing these parameters is also made a study of the mutual coupling between the elements of the array. To accomplish this study, were designed a microstrip antenna array with two patch elements, fed by a network feed. Another change made in the array was the use of the truncated ground plane, with the objective of increasing the bandwidth and miniaturize the elements of the array. In order to study the behavior of frequency selective surfaces applied in antenna arrays, three different layouts were proposed. The first layout uses the FSS as a superstrate (above the array). The second layout uses the FSS as reflector element (below the array). The third layout is placed between two FSS. Numerical and experimental results for each of the proposed configurations are presented in order to validate the research

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

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Análise de superfícies seletivas de freqüência através do método dos potenciais vetorias de Hertz

This work presents a theoretical and numerical analysis of Frequency Selective Surfaces (FSS) with elements as rectangular patch, thin dipole and crossed dipole mounted on uniaxial anisotropic dielectric substrate layers for orientations of the optical axis along x, y and z directions. The analysis of these structures is accomplished by combination of the Hertz vector potentials method and the Galerkin's technique, in the Fourier transform-domain, using entire¬domain basis functions. This study consists in the use of one more technique for analysis of FSS on anisotropic dielectric substrate. And presents as the main contribution the introduction of one more project parameter to determinate the transmission and reflection characteristics of periodic structures, from the use of anisotropic dielectric with orientations of the crystal optical axis along x, y and z directions. To validate this analysis, the numerical results of this work are compared to those obtained by other authors, for FSS structures on anisotropic and isotropic dielectric substrates. Also are compared experimental results and the numerical correspondent ones for the FSS isotropic case. The technique proposed in this work is accurate and efficient. ln a second moment, curves are presented for the transmission and reflection characteristics of the FSS structures using conducting patch elements mounted on uniaxial anisotropic dielectric substrate layers with optical axis oriented along x, y and z directions. From analysis of these curves, the performance of the considered FSS structures as function of the optical axis orientation is described

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

Contribuição para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)

This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

Desenvolvimento de uma arquitetura em hardware prototipada em FPGA para aplicações genéricas utilizando redes neurais artificiais embarcadas

This work proposes hardware architecture, VHDL described, developed to embedded Artificial Neural Network (ANN), Multilayer Perceptron (MLP). The present work idealizes that, in this architecture, ANN applications could easily embed several different topologies of MLP network industrial field. The MLP topology in which the architecture can be configured is defined by a simple and specifically data input (instructions) that determines the layers and Perceptron quantity of the network. In order to set several MLP topologies, many components (datapath) and a controller were developed to execute these instructions. Thus, an user defines a group of previously known instructions which determine ANN characteristics. The system will guarantee the MLP execution through the neural processors (Perceptrons), the components of datapath and the controller that were developed. In other way, the biases and the weights must be static, the ANN that will be embedded must had been trained previously, in off-line way. The knowledge of system internal characteristics and the VHDL language by the user are not needed. The reconfigurable FPGA device was used to implement, simulate and test all the system, allowing application in several real daily problems

Efeito da inclinação do plano de terra para antenas de microfita multicamadas com elementos parasitas fractais

This work presents the analysis of an antenna of fractal microstrip of Koch with dielectric multilayers and inclinations in the ground plane, whose values of the angles are zero degree (without inclinations), three, seven and twelve degrees. This antenna consists of three dielectric layers arranged vertically on each other, using feeding microstrip line in patch 1, of the first layer, which will feed the remaining patches of the upper layers by electromagnetic coupling. The objective of this work is to analyze the effects caused by increase of the angle of inclination of the ground plane in some antenna parameters such as return loss, resonant frequency, bandwidth and radiation pattern. The presented results demonstrate that with the increase of the inclination angle it is possible to get antennas with characteristics multiband, with bigger bandwidth, and improving the impedance matching for each case analyzed, especially the larger angle

Desacoplamento de um gerador síncrono através de um controle adaptativo por modelo de referência baseado em funções de Base radial

An alternative nonlinear technique for decoupling and control is presented. This technique is based on a RBF (Radial Basis Functions) neural network and it is applied to the synchronous generator model. The synchronous generator is a coupled system, in other words, a change at one input variable of the system, changes more than one output. The RBF network will perform the decoupling, separating the control of the following outputs variables: the load angle and flux linkage in the field winding. This technique does not require knowledge of the system parameters and, due the nature of radial basis functions, it shows itself stable to parametric uncertainties, disturbances and simpler when it is applied in control. The RBF decoupler is designed in this work for decouple a nonlinear MIMO system with two inputs and two outputs. The weights between hidden and output layer are modified online, using an adaptive law in real time. The adaptive law is developed by Lyapunov s Method. A decoupling adaptive controller uses the errors between system outputs and model outputs, and filtered outputs of the system to produce control signals. The RBF network forces each outputs of generator to behave like reference model. When the RBF approaches adequately control signals, the system decoupling is achieved. A mathematical proof and analysis are showed. Simulations are presented to show the performance and robustness of the RBF network