6533b852fe1ef96bd12aae51
RESEARCH PRODUCT
Soft Sensor Transferability between Lines of a Sulfur Recovery Unit
Francesco CurreriMaria Gabriella XibiliaLuca Patanésubject
Mathematical modelComputer sciencemedia_common.quotation_subjectProcess (computing)transferable soft sensor; nonlinear model; recurrent neural network; monitoring; prediction; inferential modelControl engineeringpredictionSoft sensorParallelRefineryNonlinear systemmonitoringRecurrent neural networkinferential modelControl and Systems Engineeringnonlinear modelrecurrent neural networkFunction (engineering)media_commontransferable soft sensordescription
Abstract Soft Sensors (SSs) are mathematical models that allow real-time estimation of hard-to-measure variables as a function of easy-to-measure ones in an industrial process, emulating the behavior of existing sensors when they are, for instance, taken off for maintenance. The Sulfur Recovery Unit (SRU) from a refinery is taken in exam. Recurrent Neural Networks (RNN) can capture the nonlinearity of such process but present a high complexity training and a very time-consuming structure optimization. For this reason, strategies to use pre-existing models are here examined by testing the transferability of the SSs between two parallel lines of the process.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |