Transfer learning in wastewater treatment plant control design : from conventional to long short-term memory-based controllers
Pisa, Ivan (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Morell Pérez, Antoni (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Vilanova i Arbós, Ramon (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
López Vicario, José (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)

Fecha:	2021
Resumen:	In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40. 17% to 94. 29% and from 34. 27% to 99. 71%, respectively.
Ayudas:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1202 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1670 Agencia Estatal de Investigación PID2019-105434RB-C33 Ministerio de Ciencia e Innovación TEC2017-84321-C4-4-R
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Control design ; Industrial control ; Transfer learning ; WWTP
Publicado en:	Sensors (Basel, Switzerland), Vol. 21, Issue 18 (September 2021) , art. 6315, ISSN 1424-8220

DOI: 10.3390/s21186315
PMID: 34577522

26 p, 1.3 MB

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