Department of Electronic Systems, Aalborg University, Fredrik Bajers Vej 7, Aalborg 9220, Denmark; Controls Group, Technology Innovation, Grundfos Holding A/S, Poul Due Jensens Vej 7, Bjerringbro 8850, Denmark.
Department of Electronic Systems, Aalborg University, Fredrik Bajers Vej 7, Aalborg 9220, Denmark.
Water Res. 2022 Aug 1;221:118782. doi: 10.1016/j.watres.2022.118782. Epub 2022 Jun 20.
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.
智能控制系统旨在通过实时控制，更好地利用现有容量，从而降低基础设施扩展的成本。最近传感器和先进的数据处理技术的出现，预计将改变水系统运营商的观点，从而增加部署新一代数据驱动控制解决方案的需求。为此，本文提出了一种用于合流制污水和雨水管网的基于数据驱动的控制框架。我们通过在网络中部署多个水位传感器，学习通过水位变化的湿季和旱季流量的影响。为了解决水力和水文建模相结合的挑战，我们采用基于高斯过程的预测控制工具来捕捉雨水和污水流入的动态影响，同时应用领域知识来保持水量平衡。为了展示该方法的实际可行性，我们在一个受实际污水管网拓扑启发的实验室设置上测试了控制性能。我们将我们的方法与目前运营所提出网络的水务公司使用的基于规则的控制器进行了比较。总体而言，该控制器学习了污水负荷和网络的时间动态，因此显著优于基准控制器，特别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施中的优缺点。
