Data selection methods for Soft Sensor design based on feature extraction
| Autor: | Graziani Salvatore, Maria Gabriella Xibilia, Riccardo Caponetto |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science Data selection. Neural Networks Feature extraction Model identification Soft Sensors 020208 electrical & electronic engineering 02 engineering and technology computer.software_genre Soft sensor Refinery 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Data mining Raw data computer Data selection |
| Zdroj: | IFAC-PapersOnLine. 53:132-137 |
| ISSN: | 2405-8963 |
| Popis: | Data selection is a critical issue in data-driven soft sensor design. The paper proposes a new method for data selection based on a feature extraction step, followed by data selection algorithms. The method has been applied to an industrial case study, i.e., the estimation of the quality of processed wastewater produced by a Sour Water Stripping plant working in a refinery. The paper reports the results obtained with different data selection algorithms. The comparison has been performed both by using raw data and the feature extraction phase. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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