Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland; Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093, Zurich, Switzerland.
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland.
Water Res. 2019 Sep 15;161:639-651. doi: 10.1016/j.watres.2019.06.007. Epub 2019 Jun 14.
Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89-91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring small-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.
传感器维护既耗时又费力，是监测现场废水处理系统的瓶颈。因此，我们将经过维护和未经维护的传感器进行比较，以监测小型序批式反应器（SBR）的生物性能。传感器类型为离子选择性 pH 值、光学溶解氧（DO）和氧化还原电位（ORP），带有铂电极。我们使用工程特征创建了软传感器：pH 值的铵谷、DO 的氧化斜坡和 ORP 的亚硝酸盐斜坡。四个基于未经维护的 pH 传感器的软传感器正确识别了铵氧化的完成（在 107 个循环中的 89-91 次），与基于维护的 pH 传感器的软传感器（在 107 个循环中的 91 次）一样多。相比之下，使用维护传感器数据的 DO 软传感器的检测性能略好（在 96 个循环中为 89 次），而使用两个未经维护的传感器数据的 DO 软传感器的检测性能分别为 77 和 82 次正确。此外，使用维护数据的 DO 软传感器对截止频率和斜率容差的优化不那么敏感，而使用未经维护数据的软传感器则更敏感。亚硝酸盐斜坡对亚硝酸盐氧化状态没有提供有用的信息，因此在这种情况下，无法对维护和非维护 ORP 传感器进行比较。在为非维护传感器设计软传感器时，我们确定了两个障碍：i）传感器类型和设计特有的恶化会影响性能。ii）软传感器的特征工程是传感器类型特定的，其结果受到曝气率等操作参数的强烈影响。总之，所提供的软传感器的结果表明，监测 SBR 的性能不一定需要频繁的传感器维护。没有传感器维护，监测小型 SBR 变得可行，这可以大大提高无人值守现场处理系统的可靠性。
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