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High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

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Published on: June 27, 2025

Sewer system flow components identification using signal processing.

F A Dorval¹, B Chocat, E Emmanuel

¹Université de Lyon, INSA Lyon, LGCIE-Laboratoire de Génie Civil et d'Ingénierie Environnementale, Villeurbanne Cedex, France. farah-altagracia.dorval@insa-lyon.fr

Water Science and Technology : a Journal of the International Association on Water Pollution Research

|July 3, 2010

Summary

This study introduces a novel signal processing method using wavelet analysis to identify dry weather flow components in sewer systems. This approach aids in understanding sewer discharge and detecting inflows for better system management.

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Area of Science:

Environmental Engineering
Hydrology
Signal Processing

Background:

Accurate sewer system modeling requires detailed flow component data.
Identifying dry weather flow (DWF) is crucial for understanding sewer behavior.
Existing methods may lack the precision needed for complex sewer networks.

Purpose of the Study:

To develop and apply a novel signal processing method for identifying DWF components in a separated stormwater sewer system.
To utilize long time series data with a 2-minute time step for enhanced analysis.
To demonstrate the capability of wavelet analysis in characterizing sewer flow components and detecting inflows.

Main Methods:

Collected time series data including flow rate, conductivity, pH, and turbidity at a 2-minute interval.
Applied wavelet analysis to filter noise and identify distinct signal components within the DWF.
Integrated hydrological modeling to further characterize identified flow components and inflows.

Main Results:

Successfully identified key components contributing to dry weather flow in the industrial catchment's sewer system.
Demonstrated the effectiveness of wavelet analysis in distinguishing DWF signals from background noise.
Developed a method for detecting extraneous inflows into the sewer system.

Conclusions:

Wavelet analysis provides a robust method for dissecting complex sewer flow data.
The developed techniques offer a foundation for improved sewer system monitoring and management.
This approach can enhance the accuracy of continuous sewer system models.