This paper deals with the robust on-line diagnosis of abnormal situations in an industrial continuous styre11e polymerization reactor through a bank of unknown input observers (UIO) that supervise changes on the most relevant process parameters and external disturbances. A model predictive control (MPC) scheme is implemented aiming al to stabilize ihe system. This may become an additional difficulty because the detrimental effects of the feedback control on the detection of abnormal situations. In the design of the UIO's a lir.earized model of the process is utilized. The observers are tuned to supervise the change of a particular parameter of !he reactor model. The procedure takes into account possible uncenainties in these parameters such that a robust diagnosis strategy of the abnormal siiuation is obtained. Simulation results show a very promising perspective to ihe proposed strategy...

This paper deals with the robust on-line diagnosis of abnormal situations in an industrial continuous styre11e polymerization reactor through a bank of unknown input observers (UIO) that supervise changes on the most relevant process parameters and external disturbances. A model predictive control (MPC) scheme is implemented aiming al to stabilize ihe system. This may become an additional difficulty because the detrimental effects of the feedback control on the detection of abnormal situations. In the design of the UIO's a lir.earized model of the process is utilized. The observers are tuned to supervise the change of a particular parameter of !he reactor model. The procedure takes into account possible uncenainties in these parameters such that a robust diagnosis strategy of the abnormal siiuation is obtained. Simulation results show a very promising perspective to ihe proposed strategy...