Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Estudo de estratégias de otimização para poços de petróleo com elevação por bombeio de cavidades progressivas

The progressing cavity pump artificial lift system, PCP, is a main lift system used in oil production industry. As this artificial lift application grows the knowledge of it s dynamics behavior, the application of automatic control and the developing of equipment selection design specialist systems are more useful. This work presents tools for dynamic analysis, control technics and a specialist system for selecting lift equipments for this artificial lift technology. The PCP artificial lift system consists of a progressing cavity pump installed downhole in the production tubing edge. The pump consists of two parts, a stator and a rotor, and is set in motion by the rotation of the rotor transmitted through a rod string installed in the tubing. The surface equipment generates and transmits the rotation to the rod string. First, is presented the developing of a complete mathematical dynamic model of PCP system. This model is simplified for use in several conditions, including steady state for sizing PCP equipments, like pump, rod string and drive head. This model is used to implement a computer simulator able to help in system analysis and to operates as a well with a controller and allows testing and developing of control algorithms. The next developing applies control technics to PCP system to optimize pumping velocity to achieve productivity and durability of downhole components. The mathematical model is linearized to apply conventional control technics including observability and controllability of the system and develop design rules for PI controller. Stability conditions are stated for operation point of the system. A fuzzy rule-based control system are developed from a PI controller using a inference machine based on Mandami operators. The fuzzy logic is applied to develop a specialist system that selects PCP equipments too. The developed technics to simulate and the linearized model was used in an actual well where a control system is installed. This control system consists of a pump intake pressure sensor, an industrial controller and a variable speed drive. The PI control was applied and fuzzy controller was applied to optimize simulated and actual well operation and the results was compared. The simulated and actual open loop response was compared to validate simulation. A case study was accomplished to validate equipment selection specialist system

Uma proposta de regulador nebuloso para geradores eólicos de indução

This work presents a proposal for a voltage and frequency control system for a wind power induction generator. It has been developed na experimental structure composes basically by a three phase induction machine, a three phase capacitor and a reactive static Power compensator controlled by histeresys. lt has been developed control algorithms using conventional methods (Pl control) and linguistic methods (using concepts of logic and fuzzy control), to compare their performances in the variable speed generator system. The control loop was projected using the ADJDA PCL 818 model board into a Pentium 200 MHz compu ter. The induction generator mathematical model was studied throught Park transformation. It has been realized simulations in the Pspice@ software, to verify the system characteristics in transient and steady-state situations. The real time control program was developed in C language, possibilish verify the algorithm performance in the 2,2kW didatic experimental system

Controle preditivo neural aplicado à processos petroquímicos

A pesquisa tem como objetivo desenvolver uma estrutura de controle preditivo neural, com o intuito de controlar um processo de pH, caracterizado por ser um sistema SISO (Single Input - Single Output). O controle de pH é um processo de grande importância na indústria petroquímica, onde se deseja manter constante o nível de acidez de um produto ou neutralizar o afluente de uma planta de tratamento de fluidos. O processo de controle de pH exige robustez do sistema de controle, pois este processo pode ter ganho estático e dinâmica nãolineares. O controlador preditivo neural envolve duas outras teorias para o seu desenvolvimento, a primeira referente ao controle preditivo e a outra a redes neurais artificiais (RNA s). Este controlador pode ser dividido em dois blocos, um responsável pela identificação e outro pelo o cálculo do sinal de controle. Para realizar a identificação neural é utilizada uma RNA com arquitetura feedforward multicamadas com aprendizagem baseada na metodologia da Propagação Retroativa do Erro (Error Back Propagation). A partir de dados de entrada e saída da planta é iniciado o treinamento offline da rede. Dessa forma, os pesos sinápticos são ajustados e a rede está apta para representar o sistema com a máxima precisão possível. O modelo neural gerado é usado para predizer as saídas futuras do sistema, com isso o otimizador calcula uma série de ações de controle, através da minimização de uma função objetivo quadrática, fazendo com que a saída do processo siga um sinal de referência desejado. Foram desenvolvidos dois aplicativos, ambos na plataforma Builder C++, o primeiro realiza a identificação, via redes neurais e o segundo é responsável pelo controle do processo. As ferramentas aqui implementadas e aplicadas são genéricas, ambas permitem a aplicação da estrutura de controle a qualquer novo processo

Controle preditivo não linear baseado no modelo de Hammerstein com prova de estabilidade

The Predictive Controller has been receiving plenty attention in the last decades, because the need to understand, to analyze, to predict and to control real systems has been quickly growing with the technological and industrial progress. The objective of this thesis is to present a contribution for the development and implementation of Nonlinear Predictive Controllers based on Hammerstein model, as well as to its make properties evaluation. In this case, in the Nonlinear Predictive Controller development the time-step linearization method is used and a compensation term is introduced in order to improve the controller performance. The main motivation of this thesis is the study and stability guarantee for the Nonlinear Predictive Controller based on Hammerstein model. In this case, was used the concepts of sections and Popov Theorem. Simulation results with literature models shows that the proposed approaches are able to control with good performance and to guarantee the systems stability

Controle preditivo bilinear aplicado a um motor de indução

The present work is based on the applied bilinear predictive control applied to an induction motor. As in particular case of the technique based on predictive control in nonlinem systems, these have desperted great interest, a time that present the advantage of being simpler than the non linear in general and most representative one than the linear one. One of the methods, adopted here, uses the linear model "quasi linear for step of time" based in Generalized Predictive Control. The modeling of the induction motor is made by the Vectorial control with orientation given for the indirect rotor. The system is formed by an induction motor of 3 cv with rotor in squirregate, set in motion for a group of benches of tests developed for this work, presented resulted for a variation of +5% in the value of set-point and for a variation of +10% and -10% in the value of the applied nominal load to the motor. The results prove a good efficiency of the predictive bilinear controllers, then compared with the linear cases

Controlador preditivo generalizado multi-modelo aplicado ao controle de pressão arterial

Postsurgical complication of hypertension may occur in cardiac patients. To decrease the chances of complication it is necessary to reduce elevated blood pressure as soon as possible. Continuous infusion of vasodilator drugs, such as sodium nitroprusside (Nipride), would quickly lower the blood pressure in most patients. However, each patient has a different sensitivity to infusion of Nipride. The parameters and the time delays of the system are initially unknown. Moreover, the parameters of the transfer function associated with a particular patient are time varying. the objective of the study is to develop a procedure for blood pressure control i the presence of uncertainty of parameters and considerable time delays. So, a methodology was developed multi-model, and for each such model a Preditive Controller can be a priori designed. An adaptive mechanism is then needed for deciding which controller should be dominant for a given plant

Controle preditivo robusto baseado em desigualdades matriciais lineares aplicado a um sistema de tanques acoplados

This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol

Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo

Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC

Controle preditivo neural aplicado à processos petroquímicos

A pesquisa tem como objetivo desenvolver uma estrutura de controle preditivo neural, com o intuito de controlar um processo de pH, caracterizado por ser um sistema SISO (Single Input - Single Output). O controle de pH é um processo de grande importância na indústria petroquímica, onde se deseja manter constante o nível de acidez de um produto ou neutralizar o afluente de uma planta de tratamento de fluidos. O processo de controle de pH exige robustez do sistema de controle, pois este processo pode ter ganho estático e dinâmica nãolineares. O controlador preditivo neural envolve duas outras teorias para o seu desenvolvimento, a primeira referente ao controle preditivo e a outra a redes neurais artificiais (RNA s). Este controlador pode ser dividido em dois blocos, um responsável pela identificação e outro pelo o cálculo do sinal de controle. Para realizar a identificação neural é utilizada uma RNA com arquitetura feedforward multicamadas com aprendizagem baseada na metodologia da Propagação Retroativa do Erro (Error Back Propagation). A partir de dados de entrada e saída da planta é iniciado o treinamento offline da rede. Dessa forma, os pesos sinápticos são ajustados e a rede está apta para representar o sistema com a máxima precisão possível. O modelo neural gerado é usado para predizer as saídas futuras do sistema, com isso o otimizador calcula uma série de ações de controle, através da minimização de uma função objetivo quadrática, fazendo com que a saída do processo siga um sinal de referência desejado. Foram desenvolvidos dois aplicativos, ambos na plataforma Builder C++, o primeiro realiza a identificação, via redes neurais e o segundo é responsável pelo controle do processo. As ferramentas aqui implementadas e aplicadas são genéricas, ambas permitem a aplicação da estrutura de controle a qualquer novo processo

O processo avaliativo institucional de desempenho do sistema público municipal de Teresina-PI (2001 a 2005): entre a lógica da excelência e a lógica da aprendizagem

This work focalize the institutional and educational evaluation, aiming to investigate the Municipal System Institutional Evaluation Performance of Teresina City Piauí (2001-2005), and to reflect about Institutional System Performance and its contribution to compose a new learning evaluation practice. In this sense, classifies elements about the evaluation practice in two Elementary Education municipal public schools, involving Education Municipal Bureau technicians as managers, pedagogues, teachers and students. Based on the ethnographic studies principles in the educational area, the work employs investigative procedures like document analysis, interviews with groups and individuals and also participator s comments. Intending to comprehend the complexity produced by the institutional and education evaluation processes, the wok reveals the Institutional Evaluation legal and educational political basis and the several positions assumed by the Learning Evaluation, as a classification tool or as a learning enhancement. This work points, as results, to a evaluation culture bipolarity carried out by the Municipal Education System as a explicit control and regulation toll, related to the classification and learning in a interaction process that operates both in the pressure and the reflection, as a culture practice established between excellence of logic and learning. The evaluation history has been construct on the evaluation actions dialectics, joint simultaneously between the Institutional Evaluation Performance and the learning evaluation. The senses, meanings and actions bipolarity is a interaction process product sustained between the institutional evaluation, under the scholar ranking application, and the learning evaluation. In this relativity, the teacher evaluation practice is found, ruled by interesting, thoughts and actions on the school evaluation, allowing a higher security and support to the learning results. Grounded in the researched reality, its possible to say that the teacher s evaluation practice is diversified, with different characteristics, when it is done in the learning search and in the learning intention. In the first case, reflects, rearranges and constructs new actions that lead the student to produce learning. In the second, shows the will, the wish of learning, but is a weak action, producing a not really significant learning and development; as a result, remains the mark approach as a determinant in the student future. The work s hope is to contribute not just to rethink these two evaluations dimensions the institutional and the learning ones but also to organize the school and to improve the pedagogic process

Determinação dos parâmetros do controlador preditivo generalizado com função de pesos variante no tempo através de algoritmos genéticos

The predictive control technique has gotten, on the last years, greater number of adepts in reason of the easiness of adjustment of its parameters, of the exceeding of its concepts for multi-input/multi-output (MIMO) systems, of nonlinear models of processes could be linearised around a operating point, so can clearly be used in the controller, and mainly, as being the only methodology that can take into consideration, during the project of the controller, the limitations of the control signals and output of the process. The time varying weighting generalized predictive control (TGPC), studied in this work, is one more an alternative to the several existing predictive controls, characterizing itself as an modification of the generalized predictive control (GPC), where it is used a reference model, calculated in accordance with parameters of project previously established by the designer, and the application of a new function criterion, that when minimized offers the best parameters to the controller. It is used technique of the genetic algorithms to minimize of the function criterion proposed and searches to demonstrate the robustness of the TGPC through the application of performance, stability and robustness criterions. To compare achieves results of the TGPC controller, the GCP and proportional, integral and derivative (PID) controllers are used, where whole the techniques applied to stable, unstable and of non-minimum phase plants. The simulated examples become fulfilled with the use of MATLAB tool. It is verified that, the alterations implemented in TGPC, allow the evidence of the efficiency of this algorithm

Projeto e controle de um sistema de aquecimento no Laboratório de Avaliação de Medição em Petróleo

The Oil Measurement Evaluation Laboratory (LAMP), located in the Federal University of Rio Grande do Norte (UFRN), has as main goal to evaluate flow and BS&W meters, where the simulation of a bigger number of operation variable in field, guarantees a less uncertain evaluation. The objective of this work is to purpose a heating system design and implementation, which will control the temperature safely and efficiently in order to evaluate and measure it. Temperature is one of the variables which influence the flow and BS&W accurate measurement, directly affecting the fluid viscosity and density in the experiment. To project the heating system it is of great importance to take the laboratory requirements, conditions and current restrictions into consideration. Three alternatives were evaluated: heat exchanger, internal resistance and external resistance. After the analyses are made in order to choose the best alternative for the heating system in the laboratory, control strategies were determined for it, PID control methods in combination with fuzzy logic were used. Results showed a better performance with fuzzy logic than with classic PID

Uma fonte chaveada de 50kW com correção de fator de potência para alimentação de uma tocha de plasma Indutiva utilizando técnicas de controle digital

This work deals with the development of an experimental study on a power supply of high frequency that provides the toch plasmica to be implemented in PLASPETRO project, which consists of two static converters developed by using Insulated Gate Bipolar Transistor (IGBT). The drivers used to control these keys are triggered by Digital Signal Processor (DSP) through optical fibers to reduce problems with electromagnetic interference (EMI). The first stage consists of a pre-regulator in the form of an AC to DC converter with three-phase boost power factor correction which is the main theme of this work, while the second is the source of high frequency itself. A series-resonant inverter consists of four (4) cell inverters operating in a frequency around 115 kHz each one in soft switching mode, alternating itself to supply the load (plasma torch) an alternating current with a frequency of 450 kHz. The first stage has the function of providing the series-resonant inverter a DC voltage, with the value controlled from the power supply provided by the electrical system of the utility, and correct the power factor of the system as a whole. This level of DC bus voltage at the output of the first stage will be used to control the power transferred by the inverter to the load, and it may vary from 550 VDC to a maximum of 800 VDC. To control the voltage level of DC bus driver used a proportional integral (PI) controller and to achieve the unity power factor it was used two other proportional integral currents controllers. Computational simulations were performed to assist in sizing and forecasting performance. All the control and communications needed to stage supervisory were implemented on a DSP

Lógica Fuzzy para Controle de pH em um Processo Petrolífero

This work proposes the design, the performance evaluation and a methodology for tuning the initial MFs parameters of output of a function based Takagi-Sugeno-Kang Fuzzy-PI controller to neutralize the pH in a stirred-tank reactor. The controller is designed to perform pH neutralization of industrial plants, mainly in units found in oil refineries where it is strongly required to mitigate uncertainties and nonlinearities. In addition, it adjusts the changes in pH regulating process, avoiding or reducing the need for retuning to maintain the desired performance. Based on the Hammerstein model, the system emulates a real plant that fits the changes in pH neutralization process of avoiding or reducing the need to retune. The controller performance is evaluated by overshoots, stabilization times, indices Integral of the Absolute Error (IAE) and Integral of the Absolute Value of the Error-weighted Time (ITAE), and using a metric developed by that takes into account both the error information and the control signal. The Fuzzy-PI controller is compared with PI and gain schedule PI controllers previously used in the testing plant, whose results can be found in the literature.