Identificação de uma coluna de destilação de metanol-água através de modelos paramétricos e redes neurais artificiais

Abstract

This work presents a black box identification for a continuous methanol-water distillation column setting in open loop and closed loop response. Step changes and Pseudo-Random Binary Signal (PRBS) disturbance were used to excite the plant. The mathematical models candidates to identify were the Artificial Neural Networks (ANN) and the parametric models: ARX(autoregressive with exogenous inputs ), ARMAX (AutoRegressive Moving Average with eXogenous inputs ), OE(Output Error) and the Box-Jenkins (BJ)structure. The closed loop configuration was the R-V. The results showed that for the bottom loop, the best response were given by BJ, OE and RNA for both open and closed loop response. For the top closed loop, the best responses were also given by BJ, OE and RNA while in open loop condition, the RNA was the one that gave satisfactory outcome. It was verified that the pseudo-random binary signal was a good choice of excitation signal in identification for both open loop and closed dynamic systems.