Benchmarking soft-sensors for remote monitoring of on-site wastewater treatment plants

Abstract

On-site wastewater treatment plants are usually unattended, so undetected failures often lead to prolonged periods of reduced performance. To stabilize the good 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 quantities sensed 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 sludge age 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, monitoring, and control are necessary to achieve robust accuracy and to obviate case-specific fine-tuning. Additionally, we provide a unique labelled dataset for further feature and data-driven soft-sensor development. Our approach is limited to sequencing batch reactors. Moreover, nitrite accumulation and alkalinity limitation cannot be detected. The strength of the approach is that unmaintained sensors drastically reduce monitoring costs, enabling the monitoring of plants hitherto unchecked.

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

“…Because we did not want to reproduce a specific treatment plant but compare a range of operational parameters instead, the parameters defining influent composition, stoichiometry, and kinetics were left at the default values given by the model. The exact settings for every run are documented in the data package, 29 including model options, modified parameters, input, model, SBR, influent, effluent, and sludge characterization. The results of the simulation are also included in the data package.…”

“…The dataset from all three plants is available at https://doi.org/ 10.25678/000194. 29 All python scripts are available at https://gitlab.com/sbrml/ integratedmonitoring. 47…”

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

“…222 However, a study exploring benchmark sensors for on-site monitoring in a WW treatment plant reveals that proper operation setups (e.g., solid retention time and aeration rate) can maintain and even improve the sensor accuracy, and integration of a sensor surveillance network and control can enhance the robustness of remote monitoring systems. 223 In conventional centralized water systems, water network distribution depends on a centralized cloud model that has a potential risk of exposure to cyberattack with the growing number of network nodes. 224 From the standpoint of security and privacy, each end-user may act as a bottleneck or a failure point to disrupt the entire water network.…”

“…225 LTCM can break centralized water systems into smaller units based on regions and functions, within which water parameters become more specific and data throughputs will be more tolerable. 223,226 Furthermore, the dynamics of multiple water systems (e.g., decentralized water treatment units and water networks) can be associated digitally through data flow (Figure 4a). As elementary water distribution units become smaller, LTCM sensor networks can be operated compatibly with the water system configuration, which enables real-time monitoring and adjustment of water quality parameters and water facility operations, expands the security capability of decentralized water systems, and protects the water sensor networks from cyberattack.…”

How well can inaccurate sensors quantify and improve the performance of a fleet of on-site wastewater treatment plants?

“…Only few measurement campaigns assess the performance of OSTs by analysing grab samples, [13][14][15][16] even less use online sensors for close to continuous measurements. [17][18][19] Thus, information to characterise performance, OST reliability and failure rates remain sparse, despite calls for centralised remote monitoring and control of OSTs issued already in 1998. 20 Therefore, a decisive factor for successful implementation of OSTs is an online monitoring concept.…”