In this paper two Soft Sensors, for the estimation of pollutants in the output flow of a Sour Water Stripping plant, are described. The plant operates in a large refinery in Italy. The Soft Sensors have been implemented by non linear data-driven approaches, by using neural networks. In order to face the issue of different sampling intervals of process and quality variables, a deep learning approach has been adopted. The deep learning approach allowed for exploiting all recorded historical data, solving the typical problem of data scarcity. For sake of comparison, traditional MLP based models have been also identified. Reported results show that the proposed approach improves the performance of neural network based Soft Sensors.
|Titolo:||A deep learning based soft sensor for a sour water stripping plant|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|
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|07969924-A deep learning based soft sensor for a sour water stripping plant.pdf||Versione Editoriale (PDF)||Administrator|