Optimizing Chlorination in Water Distribution Systems via Surrogate-assisted Neuroevolution

2026-02-07

AI Summary

This study addresses the challenge of maintaining residual chlorine levels in large-scale, heterogeneous water distribution networks, where nonlinear hydraulics, measurement noise, and high computational costs hinder effective control. The authors propose a surrogate-assisted neuroevolutionary approach that uniquely integrates the NEAT algorithm with the NSGA-II multi-objective optimizer. Leveraging a neural network surrogate model trained on EPANET simulations, the method dynamically adjusts chlorine injection at critical nodes and time steps to simultaneously optimize chlorine dosage, concentration uniformity, safety constraints, and temporal distribution. The evolved controllers outperform reinforcement learning baselines such as PPO across multiple metrics, yielding diverse, deployable Pareto-optimal chlorination strategies. This framework effectively overcomes the computational bottlenecks of high-fidelity simulation and conventional learning methods, demonstrating its practicality and innovation for optimizing complex urban water systems.

Technology Category

• Search and Optimization: Evolutionary Computation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Ensuring the microbiological safety of large, heterogeneous water distribution systems (WDS) typically requires managing appropriate levels of disinfectant residuals including chlorine. WDS include complex fluid interactions that are nonlinear and noisy, making such maintenance a challenging problem for traditional control algorithms. This paper proposes an evolutionary framework to this problem based on neuroevolution, multi-objective optimization, and surrogate modeling. Neural networks were evolved with NEAT to inject chlorine at strategic locations in the distribution network at select times. NSGA-II was employed to optimize four objectives: minimizing the total amount of chlorine injected, keeping chlorine concentrations homogeneous across the network, ensuring that maximum concentrations did not exceed safe bounds, and distributing the injections regularly over time. Each network was evaluated against a surrogate model, i.e. a neural network trained to emulate EPANET, an industry-level hydraulic WDS simulator that is accurate but infeasible in terms of computational cost to support machine learning. The evolved controllers produced a diverse range of Pareto-optimal policies that could be implemented in practice, outperforming standard reinforcement learning methods such as PPO. The results thus suggest a pathway toward improving urban water systems, and highlight the potential of using evolution with surrogate modeling to optimize complex real-world systems.

Problem

• water distribution systems
• chlorination optimization
• disinfectant residuals
• microbiological safety
• nonlinear dynamics

Innovation

• neuroevolution
• surrogate modeling
• multi-objective optimization
• water distribution systems
• chlorine dosing