Real-time quantification of the ammonium content in water resource recovery facilities (WRRFs) has received attention in recent years for both monitoring and process control.
The
increasing demand for online sensors applied to advanced control
strategies in water resource recovery facilities has resulted in the
increasing investigation of fault-detection methods to improve the
reliability of sensors installed in harsh environments. The study
herein focuses on the fault detection of ammonium sensors, especially
for effluent monitoring, given their potential in ammonium-based aeration
control applications. An artificial neural network model was built
to predict the ammonium content in the effluent by employing the information
from five other sensors installed in the activated sludge tank: NH4
+, pH, ORP, DO, and TSS. The residual between the
model prediction and the effluent ammonium sensor signal was utilized
in a fault-detection mechanism based on principal component analysis
and Shewhart monitoring charts. In contrast to previous studies, the
present work utilizes typical faults collected from a 1 year historic
dataset of an actual sensor setup. Treatment process anomalies, calibration
bias faults, and fouling drifts were the most common issues identified
from the historic dataset, and they were promptly identified by the
proposed fault-detection methodology. Once a fault is detected, the
model prediction can be actively used in place of the sensor for process
control without affecting the treatment process by utilizing faulty
datasets.
ISE-ammonium sensors subject to fouling display an increase in response time and an inexorable degradation of the measurement accuracy. The fouling morphology and composition were also studied by EDX analysis.
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