A low-cost soft sensor for sewer flow monitoring - Learning from water level measurements in manholes.

Flow meters are commonly used in manholes to monitor the flow rate for sewer operation and management. However, the large-scale deployment of flow meters in a sewer system is cost-prohibitive due to their high costs and the need for frequent maintenance. This paper proposes a soft sensor that estimates flow rates based on water level measurements in a manhole.

Identifying periods impacted by sewer inflow and infiltration using time series anomaly detection.

Accurate diagnosis of sewer inflow and infiltration (I/I) is crucial for ensuring the safe transportation of sewage and the stability of wastewater treatment processes. Identifying periods impacted by I/I is essential for I/I diagnosis, but current methods lack a standard criterion and require adaptation to specific conditions, resulting in low accuracy, complexity, and limited generalizability. This paper proposes a novel approach to distinguish I/I periods from time series of sewer measurements based on anomaly detection theory through an iterative use of a time-series reconstruction model.

A data-driven method for estimating sewer inflow and infiltration based on temperature and conductivity monitoring.

Quantitation of sewer inflow and infiltration (I/I) is important for maintaining efficient wastewater transport and treatment. I/I flows can be quantified based on flow rate and water quality measurements. Flow rate-based methods require continuous monitoring of flow rates using flow meters that are costly and prone to fouling.

An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants.

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict NO emissions. The hybrid model was successfully implemented and validated with the NO emission data from a full-scale WWTP.