Monitoring and detecting faults in wastewater treatment plants using deep learning.
Environ Monit Assess
; 192(2): 148, 2020 Jan 29.
Article
en En
| MEDLINE
| ID: mdl-31997006
ABSTRACT
Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Monitoreo del Ambiente
/
Aguas Residuales
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Environ Monit Assess
Asunto de la revista:
SAUDE AMBIENTAL
Año:
2020
Tipo del documento:
Article
País de afiliación:
Italia