Stabilizing control of a urine nitrification process in the presence of sensor drift

Thürlimann, Christian M.; Udert, Kai M.; Morgenroth, Eberhard; Villez, Kris

doi:10.1016/j.watres.2019.114958

Cited by 9 publications

(8 citation statements)

References 27 publications

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“…This idea seems promising, as intermittent addition of urine has been used to create dynamics that ensure accurate monitoring in other types of plants. 17 3.3. Synthesis from Modeling and Monitoring.…”

Section: Resultsmentioning

confidence: 99%

“…This trade-off between (a) flexibility in the treatment process choice and operational efficiency and (b) information richness of the sensor signals, which in turn influences the accuracy of the soft sensors. This kind of trade-off also exists in flow-through wastewater treatment systems 17 and is often used in explore−exploit strategies in robotic applications. 36 3.5.…”

Section: Resultsmentioning

confidence: 99%

“…The latter could enable additional process control by dosing urine. This idea seems promising, as intermittent addition of urine has been used to create dynamics that ensure accurate monitoring in other types of plants …”

Section: Resultsmentioning

confidence: 99%

“…This kind of trade-off also exists in flow-through wastewater treatment systems 26 and is often used in explore-exploit strategies in robotic applications. 30 Consequently, the soft-sensors used here are only designed for SBRs, as these plants provide the necessary dynamics for the soft-sensors to work.…”

Section: 5mentioning

confidence: 99%

“…The claims in Thurlimann et al 17 and Schneider et al 3 are quite extraordinary, namely, that accurate soft-sensing is feasible with sensor signals of questionable quality, even in the presence of a sensor drift. 18 Therefore, the key question now is under which process conditions can we apply the developed soft sensors, that is, how robust is the soft sensor to application in other plants or operating conditions?…”

Section: Introductionmentioning

confidence: 99%

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

Schneider

Furrer

Sprenger

et al. 2020

Environ. Sci. Technol.

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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 softsensors 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. AbbreviationsASM activated sludge model COD chemical oxygen demand DO dissolved oxygen DOC dissolved organic carbon SL standard-liter OST on-site wastewater treatment plant (small, unstaffed wastewater treatment plant) PE population equivalent SA sludge age SAC254 spectral absorption coefficient at 254 nanometers SBR sequencing batch reactor WWTP wastewater treatment plant

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: 5mentioning

confidence: 99%