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Related Experiment Video

Updated: Sep 3, 2025

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Chlorine Concentration Modelling and Supervision in Water Distribution Systems.

Ramon Pérez1,2, Albert Martínez-Torrents2, Manuel Martínez2,3

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|July 28, 2022
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Summary

This study presents a new method for calibrating chlorine decay models in water distribution networks (WDN). It uses real-time data to ensure accurate chlorine levels and minimize disinfection byproducts (DBPs).

Keywords:
chlorinedecay modelmodellingsupervisionwater distribution networks

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Area of Science:

  • Environmental Engineering
  • Water Treatment Technologies
  • Water Quality Management

Background:

  • Drinking water quality is a primary concern for network operators.
  • Chlorine is essential for disinfection in water distribution networks (WDN) but requires careful dosing to prevent disinfection byproducts (DBPs).
  • Existing chlorine decay models face challenges in full-scale application for WDN supervision.

Purpose of the Study:

  • To develop a robust methodology for calibrating chlorine decay models in WDN.
  • To integrate validated chlorine prediction models with extensive data analysis.
  • To improve the on-line calibration of chlorine decay models using real-time data.

Main Methods:

  • Utilized validated chlorine prediction models.
  • Conducted an intensive study of large datasets to understand parameter influence.
  • Estimated and validated model parameters using Supervisory Control and Data Acquisition (SCADA) software data from a full-scale WDN.
  • Employed off-line analytics for parameter validation.

Main Results:

  • Developed a powerful methodology for on-line calibration of chlorine decay models.
  • Demonstrated that the model calibration coherently evolves over time.
  • Identified significant variables influencing chlorine decay in WDN.

Conclusions:

  • The proposed methodology enables effective on-line calibration of chlorine decay models.
  • This approach enhances the supervision of water distribution networks by providing accurate, time-evolving chlorine predictions.
  • The study bridges the gap between theoretical models and practical application in WDN management.