Preprint has been published in a journal as an article
DOI of the published article https://doi.org/10.2166/wst.2019.301
Preprint / Version 2

Characterizing Long-term Wear and Tear of Ion-Selective pH Sensors

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DOI:

https://doi.org/10.31224/osf.io/mv6tz

Keywords:

data quality, drift, fault detection and identification, ion-selective electrodes, predictive maintenance, wastewater

Abstract

The development and validation of methods for fault detection and identification in wastewater treatment research today relies on two important assumptions: {\em (i)} that sensor faults appear at distinct times in different sensors and {\em (ii)} that any given sensor will function near-perfectly for a significant amount of time following installation. In this work, we show that such assumptions are unrealistic, at least for sensors built around an ion-selective measurement principle. Indeed, long-term exposure of sensors to treated wastewater shows that sensors exhibit important fault symptoms that appear simultaneously and with similar intensity. Consequently, our work suggests that focus of research on methods for fault detection and identification should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the very first empirically validated sensor fault model for wastewater treatment simulation and we recommend its use for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for the purpose of remote sensor validation in decentralized wastewater treatment systems.

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Posted

2018-12-17 — Updated on 2018-12-17

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