供水管网的实时监测与控制
Towards Real-Time Monitoring and Control of Water Networks
摘要：
水网络被用于许多应用中，所有这些应用都具有实时监测和控制水状态的基本任务。提出了一种用于生成适用于实时监测和控制目的的高效水网络模型的框架。所提出的模型保持了局部连通元的分布参数特性。因此，恢复了所考虑的属性的空间分辨率。网络模型的实时可行性是通过对构成组件的模型进行降阶建模来保证的。提出了一种保持模型参数依赖性的模型降阶方法。所提出的概念进行评估与水温作为考虑的属性。制定的模型被应用于预测的水温内的实验测试台的60米的示范性循环网络在公司VIEGA。一个降阶模型（ROM）与50毫米的空间分辨率，即1200离散点，被构造和利用作为识别模型的测试台的单一路径。然后，在硬件在环实验中对ROM进行了评估，用于预测下游温度，显示出较高的预测精度，平均相对误差低于3.5%。ROM单步计算时间不超过2 msec，突出了该方法的实时潜力。此外，全网络模型验证实验具有扩散和传输为主的部分进行。该网络模型能够准确地预测网络不同路径处的温度、流量和压力，平均相对误差分别小于4%、2%和2%。
英文摘要：
Water networks are used in numerous applications, all of which have the essential task of real-time monitoring and control of water states. A framework for the generation of efficient models of water networks suitable for real-time monitoring and control purposes is proposed. The proposed models preserve the distributed parameter character of the connected local elements. Hence, the spatial resolution of the property under consideration is recovered. The real-time feasibility of the network model is ensured by means of reduced order modeling of the models constituting components. A novel model order reduction procedure that preserves the model parametric dependency is introduced. The proposed concept is evaluated with the water temperature as the property under consideration. The formulated model is applied for the prediction of the water temperature within an experimental test bench of a 60 meter exemplary circulation network at the company VIEGA. A reduced order model (ROM) with a 50 mm spatial resolution, i.e. 1200 discretization points, is constructed and utilized as the identification model for a single path of the test bench. Afterwards, the ROM is evaluated in a Hardware in the Loop experiment for the prediction of the downstream temperature showing high prediction accuracy with mean relative error below 3.5 \%. The ROM single step computation time did not exceed 2 msec highlighting the real-time potential of the method. Moreover, full network model validation experiments featuring both diffusion and transport dominated parts were conducted. The network model is able to predict the temperature evolution, flow rate, and pressure accurately at the different paths of the network with mean relative errors below 4 \%, 2 \%, and 2 \%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
