Abstract
This paper presents the development and the industrial implementation of a virtual sensor (soft-sensor) in the polyethylene terephthalate (PET) production process. This soft-sensor, based on a feed-forward artificial neural network (ANN), was primarily used to provide on-line estimates of the PET viscosity, which is necessary for process control purposes. The ANN-based soft-sensor (ANN-SS) was also used for providing redundant measurements of the viscosity that could be compared to the results obtained from the process viscometer. It was shown that the proposed ANN-SS was able to adequately infer the polymer viscosity, in such a way so as this soft-sensor could be used in the real-time process control strategy. The proposed control system has successfully been applied in servo and regulatory problems, thus allowing an effective and feasible operation of the industrial plant.
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