Pressure control in water distribution networks (WDNs) is one of the interventions commonly employed to improve the reliability and sustainability of water supply. Various approaches have been proposed to solve the problem of pressure control. However, most schemes that have been proposed rely on the accuracy of a model in order to precisely control a real WDN. Therefore, any deviation between a model and real WDN parameters could render the results of control schemes useless. As a result, this work proposes the utilisation of the reinforcement learning (RL) technique to control nodes pressure in WDNs without solving the model. Quadratic approximation emulators of WDNs and RL agents are used in the proposed scheme. The effectiveness of the proposed scheme is tested on two WDNs networks and the results are compared with the conventional optimisation scheme that is commonly used for simulation cases. The results show that the proposed scheme is able to achieve the desired results when compared to the benchmark optimisation procedure. However, unlike the optimisation procedure, the proposed scheme achieved the results without the numerical solution of the WDNs. Therefore, this scheme could be used in situations where the model of a network is not well defined.

中文翻译：

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在配水网络中实现无模型压力控制

供水网络 (WDN) 中的压力控制是通常用于提高供水可靠性和可持续性的干预措施之一。已经提出了各种方法来解决压力控制问题。然而，大多数已提出的方案依赖于模型的准确性，以便精确控制真实的 WDN。因此，模型与实际 WDN 参数之间的任何偏差都可能导致控制方案的结果无用。因此，这项工作建议利用强化学习 (RL) 技术来控制 WDN 中的节点压力，而无需求解模型。WDN 和 RL 代理的二次逼近仿真器用于所提出的方案。在两个WDNs网络上测试了所提出方案的有效性，并将结果与​​通常用于模拟案例的常规优化方案进行了比较。结果表明，与基准优化程序相比，所提出的方案能够达到预期的结果。然而，与优化程序不同的是，所提出的方案在没有 WDN 数值解的情况下实现了结果。因此，该方案可用于网络模型未明确定义的情况。所提出的方案在没有 WDN 数值解的情况下获得了结果。因此，该方案可用于网络模型未明确定义的情况。所提出的方案在没有 WDN 数值解的情况下获得了结果。因此，该方案可用于网络模型未明确定义的情况。