Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect
ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Figure 1Loading Img
RETURN TO ISSUEPREVTreatment and Resour...Treatment and Resource RecoveryNEXT

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

  • Mariane Yvonne Schneider*
    Mariane Yvonne Schneider
    Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Dübendorf, Switzerland
    Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093 Zurich, Switzerland
    *Email: [email protected]
  • Viviane Furrer
    Viviane Furrer
    Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Dübendorf, Switzerland
  • Eleonora Sprenger
    Eleonora Sprenger
    Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093 Zurich, Switzerland
  • Juan Pablo Carbajal
    Juan Pablo Carbajal
    Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Dübendorf, Switzerland
    Institute for Energy Technology, University of Applied Sciences Rapperswil, 8640 Rapperswil, Switzerland
  • Kris Villez
    Kris Villez
    Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Dübendorf, Switzerland
    Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093 Zurich, Switzerland
    Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    More by Kris Villez
  • , and 
  • Max Maurer
    Max Maurer
    Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Dübendorf, Switzerland
    Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093 Zurich, Switzerland
    More by Max Maurer
Cite this: Environ. Sci. Technol. 2020, 54, 17, 10840–10849
Publication Date (Web):July 24, 2020
https://doi.org/10.1021/acs.est.9b07760
Copyright © 2020 American Chemical Society

    Article Views

    1350

    Altmetric

    -

    Citations

    LEARN ABOUT THESE METRICS
    Other access options
    Supporting Info (1)»

    Abstract

    Abstract Image

    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.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.

    Parameter tuning is used for the free parameters of the feature detection. The DO has two parameters: the slope tolerance and the cutoff frequency. The pH only has one parameter which was tuned in a previous study and here not changed any more: the cutoff frequency. For details, please refer to Schneider et al. (3)

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.9b07760.

    • Description of monitored OSTs, parameter optimization for dissolved oxygen soft sensor, further results of feature detection with synthetic data, observed failures of real-world OSTs (PDF)

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 16 publications.

    1. Mariane Yvonne Schneider, Hidenori Harada, Kris Villez, Max Maurer. Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors. Environmental Science & Technology 2023, 57 (2) , 1114-1122. https://doi.org/10.1021/acs.est.2c05945
    2. Yuankai Huang, Xingyu Wang, Wenjun Xiang, Tianbao Wang, Clifford Otis, Logan Sarge, Yu Lei, Baikun Li. Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review. Environmental Science & Technology 2022, 56 (9) , 5334-5354. https://doi.org/10.1021/acs.est.1c07857
    3. Lixia Wang, Xiang Sun, Dongfang Wang, Pengyuan Cui, Jian Wang, Qian Li. High-precision, programmable soft wireless robotics for cooling tower cleaning based on Internet of Things technology. Chemical Engineering Journal 2024, 347 , 153268. https://doi.org/10.1016/j.cej.2024.153268
    4. Selami Kilic, Abdullah Ates, Mahmut Firat, Salih Yilmaz. Developing a management and operation model for water and wastewater components using the equilibrium optimization algorithm. Water Supply 2024, 24 (3) , 643-664. https://doi.org/10.2166/ws.2024.019
    5. Daxin Zhang, Yili Wang, Jingjing Wang, Xiaoyang Fan, Shuting Zhang, Meilin Liu, Luyao Ma. Rethinking the relationships between gel like structure and sludge dewaterability based on a binary gel like structure model: Implications for the online sensing of dewaterability. Water Research 2024, 249 , 120971. https://doi.org/10.1016/j.watres.2023.120971
    6. Eva Reynaert, Deepthi Nagappa, Jürg A. Sigrist, Eberhard Morgenroth. Ensuring Microbial Water Quality for On-site Water Reuse: Importance of Online Sensors for Reliable Operation. Water Research X 2024, 42 , 100215. https://doi.org/10.1016/j.wroa.2024.100215
    7. Gang Ye, Jinquan Wan, Yuwei Bai, Yan Wang, Bin Zhu, Zhifei Zhang, Zhicheng Deng. Prediction of the effluent chemical oxygen demand and volatile fatty acids for anaerobic treatment based on different feature selections machine-learning methods from lab-scale to pilot-scale. Journal of Cleaner Production 2024, 437 , 140679. https://doi.org/10.1016/j.jclepro.2024.140679
    8. Bing Li, Siyuan Mao, Tuo Tian, Huaibin Bi, Yuxin Tian, Xueyan Ma, Yong Qiu. Design and application of soft sensors in rural sewage treatment facilities. AQUA — Water Infrastructure, Ecosystems and Society 2023, 72 (11) , 2001-2016. https://doi.org/10.2166/aqua.2023.062
    9. Eva Reynaert, Philipp Steiner, Qixing Yu, Lukas D'Olif, Noah Joller, Mariane Y. Schneider, Eberhard Morgenroth. Predicting microbial water quality in on-site water reuse systems with online sensors. Water Research 2023, 240 , 120075. https://doi.org/10.1016/j.watres.2023.120075
    10. Sandeep Rathor, Shalini Kumari. Use of machine learning & IoT for water resources management. 2023, 040014. https://doi.org/10.1063/5.0154945
    11. Mariane Yvonne Schneider, Ward Quaghebeur, Sina Borzooei, Andreas Froemelt, Feiyi Li, Ramesh Saagi, Matthew J. Wade, Jun-Jie Zhu, Elena Torfs. Hybrid modelling of water resource recovery facilities: status and opportunities. Water Science and Technology 2022, 85 (9) , 2503-2524. https://doi.org/10.2166/wst.2022.115
    12. Agnieszka Karczmarczyk, Weronika Kowalik. Combination of Microscopic Tests of the Activated Sludge and Effluent Quality for More Efficient On-Site Treatment. Water 2022, 14 (3) , 489. https://doi.org/10.3390/w14030489
    13. Faisal Jamil, Muhammad Ibrahim, Israr Ullah, Suyeon Kim, Hyun Kook Kahng, Do-Hyeun Kim. Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture. Computers and Electronics in Agriculture 2022, 192 , 106573. https://doi.org/10.1016/j.compag.2021.106573
    14. Dhrubajit Chowdhury, Alexander Melin, Kris Villez. Automatic Drift Correction through Nonlinear Sensing. 2021, 1-6. https://doi.org/10.1109/RWS52686.2021.9611798
    15. Kegong Diao. Towards resilient water supply in centralized control and decentralized execution mode. Journal of Water Supply: Research and Technology-Aqua 2021, 70 (4) , 449-466. https://doi.org/10.2166/aqua.2021.162
    16. Sandeep Rathor, Shalini Kumari. A Social Application of Artificial Intelligence & IoT for Water Conservation. IOP Conference Series: Materials Science and Engineering 2021, 1116 (1) , 012191. https://doi.org/10.1088/1757-899X/1116/1/012191