In this paper, a multi - rate decentralised model predictive control (MDMPC) approach is proposed for drinking water networks (DWN). The upper temporal layer works with a daily time scale and is in charge of achieving the global objectives , i.e., optimal selection of the water sources and the path to the reservoirs. On the other hand, the lower temporal layer is in charge of manipulating the set - point of the actuators to satisfy the local objectives, i.e., the minimisation of the energy needed for pumping water to the reservoirs. Once obtained the system decomposition into subsystems, some bidirectional flows of information between the MPC controllers that control the subsystems appea r . Hence, the proposed DMPC strategy co pes with them , resulting in a hierarchical - like scheme. Results obtained when used selected simulation scenarios over a partitioned model of the Barcelona DWN show the effectiveness of the control strategy in terms of system modularity, reduced computational burden and, at the same time, the admissible loss of performance in contrast to a centralised MPC (CMPC) strategy.

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