Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders

Missing data imputation and automatic fault detection of wastewater treatment plant (WWTP) sensors are crucial for energy conservation and environmental protection. Given the dynamic and non-linear characteristics of WWTP measurements, the conventional diagnosis models are inefficient and ignore pot...

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Vydáno v:	Chemosphere (Oxford) Ročník 288; číslo Pt 3; s. 132647
Hlavní autoři:	Ba-Alawi, Abdulrahman H., Loy-Benitez, Jorge, Kim, SangYun, Yoo, ChangKyoo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 01.02.2022
Témata:	Deep residual networks (ResNet) electric energy consumption energy conservation environmental protection Missing data imputation probability distribution Reproducibility of Results risk Sensor self-validation Smart monitoring and management Sustainable WWTP operation Variational autoencoder (VAE) wastewater treatment Water Purification Missing data imputation Sensor self-validation Variational autoencoder (VAE) Deep residual networks (ResNet) Sustainable WWTP operation Smart monitoring and management
ISSN:	0045-6535, 1879-1298, 1879-1298
On-line přístup:	Získat plný text
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