Model predictive control of water networks considering flow

Book Chapter (2017)

Book Title

Real-Time Monitoring and Operational Control of Drinking-Water Systems








Advances in Industrial Control

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Water transport networks are generally used to convey water from production plants or sources to storage tanks close to consumptions areas. Tanks are usually built with enough elevation to guarantee the service pressure required for its specific consumption area. These networks contain large water mains and control elements, such as pumping stations and valves, linking the sources to consumption areas. Their operational control of involves planning the control actions at pumping stations and valves ahead in time for periods of 24 to 48 hours, according to demand prediction. The control problem is then a resource allocation problem, with costs associated to water acquisition and treatment (production) and to electricity costs of pumping operations. Model predictive control (MPC) techniques are ideal to perform the real-time operational control of water transport networks, as they can compute, ahead of time, the best admissible control strategies for valves, pumps, or other control elements in a network to meet demands and achieve an operational goal. Typical goals in the management of water transport networks are: minimum energy consumption, cost minimization, service safety, smoothness of control actions, pressure regulation and others. This chapter will show the fundamentals of control oriented modelling in water transport networks and it will be shown , with real case studies that MPC can provide an efficient solution to predictive water resource allocation, which outperforms traditional operational management, improving the above-mentioned operational goals.


automation, control theory, optimisation.

Author keywords

water systems, flow networks, predictive control

Scientific reference

G. Cembrano, V. Puig, C. Ocampo-Martínez, M. Minoves and R. Creus. Model predictive control of water networks considering flow. In Real-Time Monitoring and Operational Control of Drinking-Water Systems, 227-249. Springer, 2017.