资源环境科技发展态势分析平台

Real-time simulation of water distribution systems (WDSs) has been applied to water resource problems ranging from engineering optimization design and operation planning and management. However, accurate implementation of real-time simulation of WDS is still a challenging task due to the limited knowledge of the large amount of varying nodal demands and pipe characteristics over the entire network system. This paper presents a self-adaptive calibration method based on Kalman filter (KF) that takes advantage of the long-term monitoring data for dual calibration of nodal demands and pipe roughness. Inferential measurements are introduced to avoid linear assumptions of the WDS system and link the hydraulics of WDS with KF. Hence, it is able to employ KF to solve the nonlinear problems in looped water distribution network. By assimilating the long-term monitoring data and adapting the calibrated parameters to various operating conditions, the framework can reduce the uncertainties caused by measurement errors and quantify the uncertainties by covariance matrixes. In addition, the presented method can help to identify abnormal WDS conditions. Three case studies have been conducted to illustrate the validity of the proposed method and its applications. The results have shown that the proposed framework is reliable and effective in practical applications.

Plain Language Summary A water distribution system (WDS) is a public infrastructure, which delivers water from water treatment plant to customers. As we can hardly monitor the condition of all pipes and nodes, hydraulic models are used to simulate the WDS. Appropriate parameters are required by the models to represent real WDS accurately, but some of these parameters need unaffordable resources to get each of them be precisely measured. Therefore, model calibration has been applied to use measured data of real WDS to estimate the parameters. Especially, real-time calibration of nodal demands and pipe roughness is of great significance for the WDS model. Data assimilation methods like Kalman filter (KF) have been used by researchers to real-time estimate parameters of different systems, but as WDS is nonlinear, the KF performs poorly in WDS parameters estimation. In this study, inferential measurements are introduced to improve the performance of KF in the nonlinear WDS. Applying inferential measurements, a self-adaptive calibration method is proposed to estimate WDS parameters of different operating conditions in real time, and to help diagnosing abnormal conditions like pipe burst and fire flow of WDS. Case studies have shown the availability of the method in real life cases.