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 concentrat...
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| Formato: | Dissertação |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/18571 |
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| Resumo: | 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 |
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