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 language||English (US)|
|Title of host publication||2022 American Control Conference, ACC 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - 2022|
|Event||2022 American Control Conference, ACC 2022 - Atlanta, United States|
Duration: Jun 8 2022 → Jun 10 2022
|Name||Proceedings of the American Control Conference|
|Conference||2022 American Control Conference, ACC 2022|
|Period||06/8/22 → 06/10/22|
Bibliographical noteFunding 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).
© 2022 American Automatic Control Council.
ASJC Scopus subject areas
- Electrical and Electronic Engineering