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.

翻译：确保大规模、异质性供水管网（WDS）的微生物学安全通常需要管理适当的消毒剂余量，包括氯。供水管网包含复杂、非线性且存在噪声的流体相互作用，这使得此类维护工作对传统控制算法而言是一个具有挑战性的难题。本文提出了一种基于神经进化、多目标优化和代理建模的进化框架来解决此问题。利用NEAT进化神经网络，以在配水管网中的战略位置和选定时间点注入氯。采用NSGA-II优化四个目标：最小化总加氯量、保持管网中氯浓度分布均匀、确保最大浓度不超过安全限值，以及使加氯操作在时间上均匀分布。每个网络均通过一个代理模型进行评估，该代理模型是一个经过训练以模拟EPANET的神经网络；EPANET是一个行业级的水力供水管网模拟器，其精度高，但计算成本过高，难以支持机器学习应用。进化得到的控制器产生了一系列多样化的帕累托最优策略，这些策略可在实践中实施，其性能优于PPO等标准强化学习方法。因此，研究结果为改善城市供水系统指明了一条路径，并突显了结合进化算法与代理建模来优化复杂现实系统的潜力。