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Model-Based Control of Water Treatment with Pumped Water Storage

Ryan Mauery, Margaret Busse, Ilya Kovalenko

TL;DR

The paper develops a control-oriented hydraulic model for a water treatment plant with pumped storage and designs a model predictive controller (MPC) to minimize emissions while meeting dynamic water demand and maintaining quality. The MPC optimizes chlorine concentration, distribution pressure, and energy emissions over a horizon of $N=5$ minutes using discrete-time nonlinear dynamics for tanks, pipes, valves, and pumps, including chlorine decay and energy-based emissions. A detailed case study contrasts MPC with a heuristic reactive controller, showing smoother pressures, reduced emissions by about $12\%$, and chlorine cycling aligned with demand. The work demonstrates a supply-side control framework that integrates energy-price variability and environmental considerations into water treatment operations, with future work on model mismatch, robustness, and experimental validation.

Abstract

Water treatment facilities are critical infrastructure they must accommodate dynamic demand patterns without system disruption. These patterns can be scheduled, such as daily residential irrigation, or unexpected, such as demand spikes from withdrawals for fire management. The critical necessity of clean, safe, and reliable water requires water treatment control strategies that are insensitive to disturbances to guarantee that demand will be met. One essential problem in achieving this is the minimization of energy costs in the process of meeting water demand, especially as the need for decarbonization persists. This work develops a control-oriented hydraulic model of a water treatment facility with integrated pumped storage and introduces a model predictive control strategy for scheduling treatment plant system operations to minimize greenhouse gas emissions and safely meet water demand.

Model-Based Control of Water Treatment with Pumped Water Storage

TL;DR

The paper develops a control-oriented hydraulic model for a water treatment plant with pumped storage and designs a model predictive controller (MPC) to minimize emissions while meeting dynamic water demand and maintaining quality. The MPC optimizes chlorine concentration, distribution pressure, and energy emissions over a horizon of minutes using discrete-time nonlinear dynamics for tanks, pipes, valves, and pumps, including chlorine decay and energy-based emissions. A detailed case study contrasts MPC with a heuristic reactive controller, showing smoother pressures, reduced emissions by about , and chlorine cycling aligned with demand. The work demonstrates a supply-side control framework that integrates energy-price variability and environmental considerations into water treatment operations, with future work on model mismatch, robustness, and experimental validation.

Abstract

Water treatment facilities are critical infrastructure they must accommodate dynamic demand patterns without system disruption. These patterns can be scheduled, such as daily residential irrigation, or unexpected, such as demand spikes from withdrawals for fire management. The critical necessity of clean, safe, and reliable water requires water treatment control strategies that are insensitive to disturbances to guarantee that demand will be met. One essential problem in achieving this is the minimization of energy costs in the process of meeting water demand, especially as the need for decarbonization persists. This work develops a control-oriented hydraulic model of a water treatment facility with integrated pumped storage and introduces a model predictive control strategy for scheduling treatment plant system operations to minimize greenhouse gas emissions and safely meet water demand.
Paper Structure (15 sections, 8 equations, 6 figures, 2 tables)

This paper contains 15 sections, 8 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Hydraulic Modeling
  4. Hydraulic Control
  5. Process Modeling
  6. Energy and Emissions Modeling
  7. Dynamic Modeling
  8. Passive Elements
  9. Active Elements
  10. Example system
  11. Model Predictive Control Design
  12. CASE STUDY
  13. Case Study Setup
  14. Case Study Results
  15. CONCLUSION

Figures (6)

  • Figure 1: Simplified water treatment process flow chart
  • Figure 2: Estimated $\phi(t)$, hourly CO2 emissions per unit energy consumed
  • Figure 3: Flow chart for example hydraulic water treatment system
  • Figure 4: Comparison of system pressures and flows for heuristic (a and c) and MPC (b and d) controlled systems.
  • Figure 5: System emissions cost comparison
  • ...and 1 more figures