Doutorado em Engenharia Elétrica
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Navegando Doutorado em Engenharia Elétrica por Assunto "Algorítmos genéticos"
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- ItemControle preditivo sintonizado via algoritmo genético aplicado em processos siderúrgicos(Universidade Federal do Espírito Santo, 2011-03-04) Almeida, Gustavo Maia de; Denti Filho, José; Salles, José Leandro Félix; Fardin, Jussara Farias; Mattedi, Alessandro; Coelho, Antônio Augusto Rodrigues; Machado, Marcelo Lucas PereiraTechniques of Model Based Predictive Control (MPC) are increasingly applied in industry because they generally exhibit a good performance and robustness, since the parameters of controller are tuned correctly. This thesis use the Genetic Algorithm (GA) to perform the tuning of parameters of the predictive controller to control mono and multivariable, linear and nonlinear models. The existing technical literature for tuning Predictive Dynamic Matrix Controller (DMC), which use a step response to control systems that are open loop stable will be compared with the tuning by GA. In the event that the process is unstable in open-loop, there is not an analytical method for the tuning. Therefore, it is noted in the literature that the use of MPC in some open-loop unstable systems, linear or not (Such as these to be studied in this thesis) is lacking, because the tuning procedure is based on trial and error and sometimes is impractical. Therefore, this study focuses the application of the MPC tuning by Genetic algorithm for two open-loop unstable processes, which are very important in the Steel Industry. The first is composed by Rolling Mill Stands, where we wish to minimize the variation of strip thickness the last stand due to disturbances that affect the process such as temperature and strip thickness variations in the first stand. In this case we use the linear and multivariable model to develop the Generalized Predictive Controller (GPC) whose parameters are tuned by GA. The second process, unstable in open-loop, is the level of Mold of a Continuous Casting, which has a nonlinear model and is therefore controlled by techniques of nonlinear predictive control using neural networks and Hammerstein model. A comparison is made between these controllers to analyze the stability and robustness when the mold is affected by disturbance of Bulging, Clogging and Argon.
- ItemModelos de séries temporais para previsão de nível de líquidos em cadinho de altos-fornos(Universidade Federal do Espírito Santo, 2016-05-20) Gomes, Flavio da Silva Vitorino; Salles, José Leandro Félix; Rodrigues, Alexandre Loureiros; Garcia, Cláudio; Fardin, Jussara Farias; Patrick Marques CiarelliThe operation of material extraction from blast furnace is carried out with a significantdegree of uncertainty, among other reasons, because the measuring level of liquids cannotbe measured directly. This thesis presents a system for forecasting the level of liquid in the blast furnace hearth by measuring the electromotive force generated in shell based on a model seasonal autoregressive integrated moving average (SARIMA). This work has shown electromotive force is a non-stationary and nonlinear time series with a strong seasonal behavior that is strongly correlated with the level of liquids. Some comparisons were made with models based on artificial neural networks with time delay (TDNN) and the results indicated that the nonlinear model has better forecasting performance. This methodology consists of the strategy for analysis, identification, filtering and prediction of the level of liquids through TDNN models achieving at the end of the process a prediction with satisfactory accuracy. The forecast level of liquids with horizon up to 1 hour ahead can help operators and engineers during the control and process optimization of the production of blast furnaces increasing safety and financial gains.