Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The quality of the treated wastewater is conditioned by the performance of wastewater treatment processes. However, real-time monitoring of quality variables in wastewater treatment plants (WWTP) is a challenging problem. In this paper, an adaptive online monitoring approach that is based on long short term memory (LSTM) neural network is proposed to estimate the bacterial concentration, mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) in WWTP. Due to the lack of a large dataset and difficulties in measuring quality variables, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is designed to generate synthetic data for training. Tuned hyperparameters are obtained for the proposed method. In addition, the performance is compared with the traditional LSTM using two datasets. Finally, the results indicate that WGAN successfully generates realistic training samples and quality variables are monitored with satisfactory performance.

Original languageEnglish (US)
Title of host publication2022 American Control Conference, ACC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3204-3211
Number of pages8
ISBN (Electronic)9781665451963
DOIs
StatePublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: Jun 8 2022Jun 10 2022

Publication series

NameProceedings of the American Control Conference
Volume2022-June
ISSN (Print)0743-1619

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period06/8/2206/10/22

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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