通过代理辅助神经进化优化水分配系统中的氯化
Optimizing Chlorination in Water Distribution Systems via Surrogate-assisted Neuroevolution
AI总结：
本文提出基于神经进化、多目标优化和代理建模的框架，用于优化水分配系统中氯气的投放策略，通过进化神经网络实现氯气在关键节点的投放，以减少总投放量并保持浓度均匀，优于传统强化学习方法。
摘要：
确保大型异质水分配系统（WDS）的微生物安全通常需要管理适当的消毒剂残余，包括氯气。WDS包含复杂的流体相互作用，是非线性和噪声的，使传统的控制算法难以维持。本文提出基于神经进化、多目标优化和代理建模的进化框架。通过NEAT进化神经网络，在分布网络中选择性地点注入氯气。NSGA-II被用于优化四个目标：最小化总氯气注入量、保持网络中氯浓度的均匀性、确保最大浓度不超过安全范围、以及在时间上均匀分布注入。每个网络都通过代理模型进行评估，即一个训练以模拟EPANET的神经网络，EPANET是行业级别的水力WDS模拟器，准确但计算成本过高，无法支持机器学习。进化控制器产生了多样化的帕累托最优策略，可以在实践中实施，优于PPO，一种标准的强化学习方法。因此，结果表明了一条改进城市水系统的方法，并突显了使用代理建模进行进化以优化复杂现实系统潜力。
英文摘要：
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 PPO, a standard reinforcement learning method. 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.
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