https://doi.org/10.25678/000194

Data for: Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants

On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.

Data and Resources

Citation

This Data Package

Schneider, M. Y., Furrer, V., Sprenger, E., Sterkele, B., Richter, S., & Rottermann, K. (2019). Data for: Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants (Version 1.0) [Data set]. Eawag: Swiss Federal Institute of Aquatic Science and Technology. https://doi.org/10.25678/000194

The associated article

Schneider, M. Y., V. Furrer, E. Sprenger, J. P. Carbajal, K. Villez, and M. Maurer (2020), Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants, Environmental Science & Technology, doi:10.1021/acs.est.9b07760.

Metadata

Open Data Open Data
Author(s)
  • Schneider, Mariane Yvonne
  • Furrer, Viviane
  • Sprenger, Eleonora
  • Sterkele, Bettina
  • Richter, Sylvia
  • Rottermann, Karin
Keywords sequencing batch reactor,wastewater treatment,soft-sensors,on-site wastewater treatment
Substances (scientific names)
  • Ammonium nitrogen (QGZKDVFQNNGYKY-UHFFFAOYSA-O)
  • Nitrate nitrogen (NHNBFGGVMKEFGY-UHFFFAOYSA-N)
  • Nitrite nitrogen (IOVCWXUNBOPUCH-UHFFFAOYSA-M)
Substances (generic terms)
  • nutrients
  • organic carbon
Systems
  • Wastewater treatment plants (3 real-world cases) plus synthetic data
Timerange
  • 2018-04 TO 2019-08
Geographic Name(s)
  • Switzerland
Review Level general
Curator Schneider, Mariane
Contact mariane.schneider@eawag.ch
DOI 10.25678/000194