Detection of Energy Drifts in Waste Water Treatment Plants Using Dynamic Clustering

Lucie Martin, Muriel Dugachard, Yuqi Wang, Guillaume Scherpereel

2024

Abstract

The sanitation process is energy intensive. There are therefore environmental issues for treated wastewater companies which must always optimize and reduce their energy expenditure. This paper aims to characterize the energy consumption patterns of the Waste Water Treatment Plants (WWTPs). Once these patterns have been established, their evolution is monitored through time. This work is based on the 78 most energy-intensive treated wastewater treatment plants in France. The consumption is studied from 2019 to the beginning of 2020. Energy expenditure depends on the operating condition of the WWTP, such as the volume of treated wastewater, the organic-based pollution, the rainfall, the amount of suspended solids, the temperature and the pH of the effluent. This relation is modeled using PLS regression, which can be used to characterize the WWTP’s energy consumption behavior. WWTPs’ load patterns are grouped into clusters using K-means. Five different consumption patterns are obtained for the year 2019. A dynamic K-means is employed to update patterns on a daily basis. Potentials drifts may have been detected thanks to the statistical distances of the treatment plants compared to the average characteristics of each of the groups.

Paper Citation

in Harvard Style

Martin L., Dugachard M., Wang Y. and Scherpereel G. (2024). Detection of Energy Drifts in Waste Water Treatment Plants Using Dynamic Clustering. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 661-670. DOI: 10.5220/0012320500003654

in Bibtex Style

@conference{icpram24,
author={Lucie Martin and Muriel Dugachard and Yuqi Wang and Guillaume Scherpereel},
title={Detection of Energy Drifts in Waste Water Treatment Plants Using Dynamic Clustering},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={661-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012320500003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Detection of Energy Drifts in Waste Water Treatment Plants Using Dynamic Clustering
SN - 978-989-758-684-2
AU - Martin L.
AU - Dugachard M.
AU - Wang Y.
AU - Scherpereel G.
PY - 2024
SP - 661
EP - 670
DO - 10.5220/0012320500003654
PB - SciTePress