Reinforcement learning techniques for the control of wastewater treatment plants

Abstract

Since water pollution is one of the most serious environmental problems today, control of wastewater treatment plants (WWTPs) is a crucial issue nowadays and stricter standards for the operation of WWTPs have been imposed by authorities. 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. Thus, it is desirable the development of autonomous systems that learn from interaction with a WWTP and that can operate taking into account changing environmental circumstances. In this paper we present an intelligent agent using reinforcement learning for the oxygen control in the N-Ammonia removal process in the well known Benchmark Simulation Model no.1 (BSM1). The aim of the approach presented in this paper is to minimize the operation cost changing the set-points of the control system autonomously. © 2011 Springer-Verlag Berlin Heidelberg.