Combining CSP and MPC for the operational control of water networks: Application to the Richmond case study

Abstract

This paper presents the combination of linear Model Predictive Control (MPC) with Constraint Satisfaction Problem (CSP) for the operational control of drinking water networks. The methodology has been divided into two functional layers: First, a CSP algorithm is used to transfer non-linear pressure equations of drinking water networks (DWNs) into linear constraints, which can enclose feasible solution of the hydraulic non-linear problem during the optimizing process. Then, a linear MPC with added linear constraints is solved to generate control strategies which consider control objectives. Finally, the proposed approach is simulated using Epanet to represent the real DWNs. PLIO, which is a generic operational tool for controlling water networks that uses non-linear MPC, is used for validation. The Richmond water system is used as a case study.