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

Abstract

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 ofmore » the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.« less

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [1]
  1. Swiss Federal Inst. of Aquatic Science and Technology (Eawag), Dübendorf (Switzerland); Eidgenoessische Technische Hochschule, Zurich (Switzerland). Inst. of Civil, Environmental and Geomatic Engineering
  2. Swiss Federal Inst. of Aquatic Science and Technology (Eawag), Dübendorf (Switzerland)
  3. Eidgenoessische Technische Hochschule, Zurich (Switzerland). Inst. of Civil, Environmental and Geomatic Engineering
  4. Swiss Federal Inst. of Aquatic Science and Technology (Eawag), Dübendorf (Switzerland); Univ. of Applied Sciences, Rapperswil (Switzerland). Inst. for Energy Technology
  5. Swiss Federal Inst. of Aquatic Science and Technology (Eawag), Dübendorf (Switzerland); Eidgenoessische Technische Hochschule, Zurich (Switzerland). Inst. of Civil, Environmental and Geomatic Engineering; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1659624
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Environmental Science and Technology
Additional Journal Information:
Journal Volume: 54; Journal Issue: 17; Journal ID: ISSN 0013-936X
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; cations; manufacturing; oxidation; sensors; water treatment

Citation Formats

Schneider, Mariane Yvonne, Furrer, Viviane, Sprenger, Eleonora, Carbajal, Juan Pablo, Villez, Kris, and Maurer, Max. Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants. United States: N. p., 2020. Web. https://doi.org/10.1021/acs.est.9b07760.
Schneider, Mariane Yvonne, Furrer, Viviane, Sprenger, Eleonora, Carbajal, Juan Pablo, Villez, Kris, & Maurer, Max. Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants. United States. https://doi.org/10.1021/acs.est.9b07760
Schneider, Mariane Yvonne, Furrer, Viviane, Sprenger, Eleonora, Carbajal, Juan Pablo, Villez, Kris, and Maurer, Max. Fri . "Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants". United States. https://doi.org/10.1021/acs.est.9b07760. https://www.osti.gov/servlets/purl/1659624.
@article{osti_1659624,
title = {Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants},
author = {Schneider, Mariane Yvonne and Furrer, Viviane and Sprenger, Eleonora and Carbajal, Juan Pablo and Villez, Kris and Maurer, Max},
abstractNote = {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.},
doi = {10.1021/acs.est.9b07760},
journal = {Environmental Science and Technology},
number = 17,
volume = 54,
place = {United States},
year = {2020},
month = {7}
}

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