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

Authors:
Félix Hernández-del-Olmo;Félix H. Llanes;Elena Gaudioso
Affiliations:
Artificial Intelligence Department, E.T.S.I. Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040 Madrid, Spain;PYCSA Infraestructuras, Francisco Sancha 8, 28034 Madrid, Spain;Artificial Intelligence Department, E.T.S.I. Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040 Madrid, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 9
Cited 0

Wastewater Treatment Systems from Case–Based Reasoning

Machine Learning - Special issue on case-based reasoning
Elements of machine learning

Elements of machine learning
Artificial Intelligence and Environmental Decision Support Systems

Applied Intelligence
Intelligent Sequence Planning for Wastewater Treatment Systems

IEEE Expert: Intelligent Systems and Their Applications
Supporting teachers in collaborative student modeling: A framework and an implementation

Expert Systems with Applications: An International Journal
The task of guiding in adaptive recommender systems

Expert Systems with Applications: An International Journal
Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks

Expert Systems with Applications: An International Journal
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	12.05

Visualization

Abstract

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 24h/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).