Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50 and 60 ) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: i) model-free and data-driven predictive control; ii) control philosophy focused on operating the tank with a variable wastewater set-point level; iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system. The results for a real case-study during 90 days show a 16.7 decrease in electrical energy consumption while still achieving a 97 reduction in the number of alarms (tank level above 7.2 meters) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.
A method is provided for operating a wastewater pumping station of a wastewater pumping network. The pumping station includes a pump, that starts pumping if a level of a wastewater in a tank exceeds a first wastewater level, and the pump stops pumping if the level of the wastewater in the tank drops below a second level. The method includes determining a magnitude of a parameter (Psys, Q, n, ΔP, Pelectrical, cos φ, I) expressing the load of the wastewater pumping network. If it is determined that the magnitude of the parameter has passed a specified threshold, the pump is activated to start pumping in an energy optimization mode. A control unit is also provided for the wastewater pumping station of the wastewater pumping network, and a system is provided for centrally controlling a plurality of pumps of wastewater pumping stations in a wastewater pumping network.