TY - GEN
T1 - Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants
AU - Alharbi, Mohammed S.
AU - Hong, Pei Ying
AU - Kirati, Taous Meriem Laleg
N1 - Funding Information:
The authors would like to acknowledge the assistance from the KAUST Facilities and Management Utilities Team. This work has been supported by the KAUST, Saudi and Center of Excellence for NEOM research at KAUST, Saudi Arabia flagship project research fund (REI/1/4178-03-01).
Publisher Copyright:
© 2022 American Automatic Control Council.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85138491519&partnerID=8YFLogxK
U2 - 10.23919/ACC53348.2022.9867166
DO - 10.23919/ACC53348.2022.9867166
M3 - Conference contribution
AN - SCOPUS:85138491519
T3 - Proceedings of the American Control Conference
SP - 3204
EP - 3211
BT - 2022 American Control Conference, ACC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 American Control Conference, ACC 2022
Y2 - 8 June 2022 through 10 June 2022
ER -