Author(s): Lara Suarez Casabiell; Pedro Simon; Joaquin Lopez; Juan Manuel Fernandez Montenegro
Linked Author(s):
Keywords: Aeration control; Decision support; Forecast; Recurrent neural networks; Water quality optimization
Abstract: Effective operation of wastewater treatment plants (WWTPs) requires timely and informed decisions to maintain process stability and energy efficiency. This paper presents two artificial intelligence (AI) tools designed to support WWTP operators in short-term decision-making. The first tool provides daily forecasts of influent water quality parameters, generating one prediction for tomorrow and another for overmorrow. These forecasts, covering key parameters such as chemical oxygen demand (COD), and ammonium, help anticipate influent variability. The second tool replicates the existing aeration control logic, incorporating additional control functionalities and short-term trend estimation for key aeration-related parameters such as ammonium and nitrate. Together, the two tools create a predictive operational framework that enhances situational awareness, supports proactive control actions, and contributes to more stable and energy-efficient WWTP performance.
Year: 2026