An emergent approach for the control of wastewater treatment plants by means of reinforcement learning techniques

One of the main problems in the automation of the control of wastewater treatment plants (WWTPs) appears when the control system does not respond as it should because of changes on influent load or flow. To tackle this difficult task, the application of Artificial Intelligence is not new, and in fact, currently Expert Systems may supervise the plant 24 h/day assisting the plant operators in their daily work. However, the knowledge of the Expert System must be elicited previously from interviews to plant operators and/or extracted from data previously stored in databases. Although this approach still has a place in the control of wastewater treatment plants, it should aim to develop autonomous systems that learn from the direct interaction with the WWTP and that can operate taking into account changing environmental circumstances. In this paper we present an approach based on an agent with learning capabilities. In this approach, the agent’s knowledge emerges from the interaction with the plant. In order to show the validity of our assertions, we have implemented such an emergent approach for the N-Ammonia removal process in a well established simulated WWTP known as Benchmark Simulation Model No.1 (BSM1).