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Disinfectant Control in Drinking Water Networks: Integrating Advection-Dispersion-Reaction Models and Byproduct Constraints

Salma M. Elsherif, Ahmad F. Taha, Ahmed A. Abokifa

Abstract

Effective disinfection is essential for maintaining water quality standards in distribution networks. Chlorination, as the most used technique, ensures safe water by maintaining sufficient chlorine residuals but also leads to the formation of disinfection byproducts (DBPs). These DBPs pose health risks, highlighting the need for chlorine injection control (CIC) by booster stations to balance safety and DBPs formation. Prior studies have followed various approaches to address this research problem. However, most of these studies overlook the changing flow conditions and their influence on the evolution of the chlorine and DBPs concentrations by integrating simplified transport-reaction models into CIC. In contrast, this paper proposes a novel CIC method that: (i) integrates multi-species dynamics, (ii) allows for a more accurate representation of the reaction dynamics of chlorine, other substances, and the resulting DBPs formation, and (iii) optimizes for the regulation of chlorine concentrations subject to EPA mandates thereby mitigating network-wide DBPs formation. The novelty of this study lies in its incorporation of time-dependent controllability analysis that captures the control coverage of each booster station. The effectiveness of the proposed CIC method is demonstrated through its application and validation via numerical case studies on different water networks with varying scales, initial conditions, and parameters.

Disinfectant Control in Drinking Water Networks: Integrating Advection-Dispersion-Reaction Models and Byproduct Constraints

Abstract

Effective disinfection is essential for maintaining water quality standards in distribution networks. Chlorination, as the most used technique, ensures safe water by maintaining sufficient chlorine residuals but also leads to the formation of disinfection byproducts (DBPs). These DBPs pose health risks, highlighting the need for chlorine injection control (CIC) by booster stations to balance safety and DBPs formation. Prior studies have followed various approaches to address this research problem. However, most of these studies overlook the changing flow conditions and their influence on the evolution of the chlorine and DBPs concentrations by integrating simplified transport-reaction models into CIC. In contrast, this paper proposes a novel CIC method that: (i) integrates multi-species dynamics, (ii) allows for a more accurate representation of the reaction dynamics of chlorine, other substances, and the resulting DBPs formation, and (iii) optimizes for the regulation of chlorine concentrations subject to EPA mandates thereby mitigating network-wide DBPs formation. The novelty of this study lies in its incorporation of time-dependent controllability analysis that captures the control coverage of each booster station. The effectiveness of the proposed CIC method is demonstrated through its application and validation via numerical case studies on different water networks with varying scales, initial conditions, and parameters.
Paper Structure (20 sections, 17 equations, 6 figures, 3 tables, 2 algorithms)

This paper contains 20 sections, 17 equations, 6 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Paper Contributions and Organization
  4. Multi-species Disinfectant and Byproducts Dynamics Model
  5. Transport and Reaction in Pipes: Advection-Dispersion-Reaction Model
  6. Advection-Dominant PDE Discretization Schemes
  7. Dispersion-Dominant PDE Discretization Schemes
  8. Pre-Simulation Preparations: Time-Step, Grid Size, and Numerical Stability Assurance
  9. Dynamic Modeling of the Transport and Reaction Dynamic in Pipes
  10. Conservation of Mass
  11. Mass Balance at Reservoirs
  12. Mass Balance at Pumps and Valves
  13. Mass Balance at Junctions
  14. Mass Balance at Tanks
  15. Chlorine Multi-Species Reaction and Decay Dynamics and DBPs Formation Models
  16. ...and 5 more sections

Figures (6)

  • Figure 1: Networks under study and their layouts: (a) BLA-M, (b) Anytown, (c) FOS, and (d) Net3 networks.
  • Figure 2: Chlorine concentrations at Junctions (a) J1 and (b) J2 of BLA-M network without (AR) and with (ADR) the consideration of the dispersion process effect.
  • Figure 3: (a) Chlorine and (b) THMs concentrations at Tank TK1 of the Anytown network without (AR) and with (ADR) the consideration of the dispersion process effect.
  • Figure 4: Controllability of chlorine injections by each booster stations allocated over the FOS network if worked solely to steer the concentrations at the target junction J12, for two hydraulic settings: (a) Hyd. #1 and (b) Hyd. #2. Colored tiles indicate full $\mathrm{rank}$ metric.
  • Figure 5: (a) Summation of MPC control actions at Reservoir R1 and Junction J3 of the BLA-M network, and the corresponding chlorine concentrations at (b) Junction J1 and (c) Junction J2 for two cases: Case #1, applying MPC without prior controllability analysis and without DBPs constraints; and Case #2, applying MPC with prior controllability analysis and DBPs constraints.
  • ...and 1 more figures

Theorems & Definitions (1)

  • Definition 1