1 research outputs found
Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors
On-site wastewater treatment plants (OSTs) often lack
monitoring,
resulting in unreliable treatment performance. They thus appear to
be a stopgap solution despite their potential contribution to circular
water management. Low-maintenance but inaccurate soft sensors are
emerging that address this concern. However, how their inaccuracy
impacts the catchment-wide treatment performance of a system of many
OSTs has not been quantified. We develop a stochastic model to estimate
catchment-wide OST performances with a Monte Carlo simulation. In
our study, soft sensors with a 70% accuracy improved the treatment
performance from 66% of the time functional to 98%. Soft sensors optimized
for specificity, indicating the true negative rate, improve the system
performance, while sensors optimized for sensitivity, indicating the
true positive rate, quantify the treatment performance more accurately.
This new insight leads us to suggest programming two soft sensors
in practical settings with the same hardware sensor data as input:
one soft sensor geared to high specificity for maintenance scheduling
and one geared to high sensitivity for performance quantification.
Our findings suggest that a maintenance strategy combining inaccurate
sensors with appropriate alarm management can vastly improve the mean
catchment-wide treatment performance of a system of OSTs