Smart control in water systems aims to reduce the cost of infrastructure expansion by better utilizing the available capacity through real-time control. The recent availability of sensors and advanced data processing is expected to transform the view of water system operators, increasing the need for deploying a new generation of data-driven control solutions. To that end, this paper proposes a data-driven control framework for combined wastewater and stormwater networks. We propose to learn the effect of wet- and dry-weather flows through the variation of water levels by deploying a number of level sensors in the network. To tackle the challenges associated with combining hydraulic and hydrologic modelling, we adopt a Gaussian process-based predictive control tool to capture the dynamic effect of rain and wastewater inflows, while applying domain knowledge to preserve the balance of water volumes. To show the practical feasibility of the approach, we test the control performance on a laboratory setup, inspired by the topology of a real-world wastewater network. We compare our method to a rule-based controller currently used by the water utility operating the proposed network. Overall, the controller learns the wastewater load and the temporal dynamics of the network, and therefore significantly outperforms the baseline controller, especially during high-intensity rain periods. Finally, we discuss the benefits and drawbacks of the approach for practical real-time control implementations.

水系统中的智能控制旨在通过实时控制更好地利用可用容量来降低基础设施扩展的成本。最近传感器和先进数据处理的可用性预计将改变水系统运营商的观点，增加部署新一代数据驱动控制解决方案的需求。为此，本文提出了一种数据驱动的废水和雨水联合网络控制框架。我们建议通过在网络中部署多个液位传感器来了解潮湿和干燥天气流量对水位变化的影响。为了解决与结合水力和水文建模相关的挑战，我们采用基于高斯过程的预测控制工具来捕捉雨水和废水流入的动态影响，同时应用领域知识来保持水量的平衡。为了展示该方法的实际可行性，我们在实验室设置上测试了控制性能，灵感来自现实世界废水网络的拓扑结构。我们将我们的方法与运营拟议网络的自来水公司当前使用的基于规则的控制器进行比较。总体而言，控制器了解废水负载和网络的时间动态，因此明显优于基线控制器，尤其是在高强度降雨期间。最后，我们讨论了该方法在实际实时控制实现中的优缺点。受现实世界废水网络拓扑的启发。我们将我们的方法与运营拟议网络的自来水公司当前使用的基于规则的控制器进行比较。总体而言，控制器了解废水负载和网络的时间动态，因此明显优于基线控制器，尤其是在高强度降雨期间。最后，我们讨论了该方法在实际实时控制实现中的优缺点。受现实世界废水网络拓扑的启发。我们将我们的方法与运营拟议网络的自来水公司当前使用的基于规则的控制器进行比较。总体而言，控制器了解废水负载和网络的时间动态，因此明显优于基线控制器，尤其是在高强度降雨期间。最后，我们讨论了该方法在实际实时控制实现中的优缺点。