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Reinforcement Learning Techniques for the Control of WasteWater Treatment Plants

  • Felix Hernandez-del-Olmo
  • Elena Gaudioso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

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.

Keywords

Expert System Wastewater Treatment Plant Reinforcement Learning Markov Decision Process Fuzzy Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Felix Hernandez-del-Olmo
    • 1
  • Elena Gaudioso
    • 1
  1. 1.Artificial Intelligence DepartmentE.T.S.I. Informatica, UNEDSpain

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