Unsupervised Learning Approach for pH Anomaly Detection in Wastewater Treatment Plants
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Diogo Gigante [1] ; Pedro Oliveira [1] ; Bruno Fernandes [1] ; Frederico Lopes [2] ; Paulo Novais [1]
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[1] Universidade do Minho
Universidade do Minho
Braga (São José de São Lázaro), Portugal
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[2] Águas do Norte (Guimarães, Portugal)
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- Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López
, Pablo García Bringas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-86271-8, págs. 588-599 - Idioma: inglés
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Resumen
Sustainability has been a concern for society over the past few decades, preserving natural resources being one of the main themes. Among the various natural resources, water was one of them. The treatment of residual waters for future reuse and release to the environment is a fundamental task performed by Wastewater Treatment Plants (WWTP). Hence, to guarantee the quality of the treated effluent in a WWTP, continuous control and monitoring of abnormal events in the substances present in this water resource are necessary. One of the most critical substances is the pH that represents the measurement of the hydrogen ion activity. Therefore, this work presents an approach with a conception, tune and evaluation of several candidate models, based on two Machine Learning algorithms, namely Isolation Forests (iF) and One-Class Support Vector Machines (OCSVM), to detect anomalies in the pH on the effluent of a multi-municipal WWTP. The OCSVM-based model presents better performance than iF-based with an approximate 0.884 of Area Under The Curve - Receiver Operating Characteristics (AUC-ROC).
