PREDICTIONS FOR A PROCESS IN AN INDUSTRIAL PLANT

To generate real-time or least near real-time predictions for a process in an industrial plant, a set of neural networks are trained to create a set of trained models. The set of trained models is then used to output the predictions, by inputting online measurement results in an original space to tw...

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Bibliographische Detailangaben
Hauptverfasser:	CORTINOVIS, Andrea, MERCANGOEZ, Mehmet
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING
Online-Zugang:	Volltext bestellen
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Beschreibung
Zusammenfassung:	To generate real-time or least near real-time predictions for a process in an industrial plant, a set of neural networks are trained to create a set of trained models. The set of trained models is then used to output the predictions, by inputting online measurement results in an original space to two trained models whose outputs are fed, as reduced space inputs and reduced space initial states, to a third trained model. The third trained model processes the reduced space inputs to reduced space predictions. They are fed to a fourth trained model, which outputs the predictions in the original space.