Machine Learning Weather Soft-Sensor for Advanced Control ofWastewater Treatment Plants

Hernández-del-Olmo, Félix, Gaudioso, Elena, Duro, Natividad y Dormido, Raquel . (2019) Machine Learning Weather Soft-Sensor for Advanced Control ofWastewater Treatment Plants. Sensors 2019, 19(14), 3139

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Título Machine Learning Weather Soft-Sensor for Advanced Control ofWastewater Treatment Plants
Autor(es) Hernández-del-Olmo, Félix
Gaudioso, Elena
Duro, Natividad
Dormido, Raquel
Materia(s) Ingeniería Informática
Abstract Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.
Palabras clave wastewater treatment plants
soft-sensors
machine learning techniques
Editor(es) MDPI
Fecha 2019
Formato application/pdf
Identificador bibliuned:95-Fhernandez-0004
http://e-spacio.uned.es/fez/view/bibliuned:95-Fhernandez-0004
DOI - identifier https://doi.org/10.3390/s19143139
ISSN - identifier 1424-8220
Nombre de la revista Sensors
Número de Volumen 19
Número de Issue 14
Publicado en la Revista Sensors 2019, 19(14), 3139
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in Sensors, is available online at the publisher's website: MDPI, https://doi.org/10.3390/s19143139
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Sensors, está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/s19143139

 
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Creado: Thu, 25 Jan 2024, 23:24:17 CET