The smart water infrastructures laboratory is a research facility at Aalborg University, Denmark. The laboratory enables experimental research in control and management of water infrastructures in a realistic environment. The laboratory is designed as a modular system that can be configured to adapt the test-bed to the desired network. The water infrastructures recreated in this laboratory are district heating, drinking water supply, and waste water collection systems. This paper focuses on the first two types of infrastructure. In the scaled-down network the researchers can reproduce different scenarios that affect its management and validate new control strategies. This paper presents four study-cases where the laboratory is configured to represent specific water distribution and waste collection networks allowing the researcher to validate new management solutions in a safe environment. Thus, without the risk of affecting the consumers in a real network. The outcome of this research facilitates the sustainable deployment of new technology in real infrastructures.

Reinforcement learning (RL) is a widely used control technique that finds an optimal policy using the feedback of its actions. The search for the optimal policy requires that the system explores a broad region of the state space. This search puts at risk the safe operation, since some of the explored regions might be near the physical system limits. Implementing learning methods in industrial applications is limited because of its uncertain behavior when finding an optimal policy. This work proposes an RL control algorithm with a filter that supervises the safety of the exploration based on a nominal model. The performance of this safety filter is increased by modeling the uncertainty with a Gaussian process (GP) regression. This method is applied to the optimization of the management of a water distribution network (WDN) with an elevated reservoir; the management objectives are to regulate the tank filling while maintaining an adequate water turnover. The proposed method is validated in a laboratory setup that emulates the hydraulic features of a WDN.