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Energies 2016, 9(9), 755; doi:10.3390/en9090755

Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning

1
Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, Spain
2
Department of Computer Sciences and Automatic Control, National Distance Education University (UNED), 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Carl-Fredrik Lindberg
Received: 3 June 2016 / Revised: 8 August 2016 / Accepted: 9 September 2016 / Published: 16 September 2016
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Abstract

Currently, energy and environmental efficiency are critical aspects in wastewater treatment plants (WWTPs). In fact, WWTPs are significant energy consumers, especially in the active sludge process (ASP) for the N-ammonia removal. In this paper, we face the challenge of simultaneously improving the economic and environmental performance by using a reinforcement learning approach. This approach improves the costs of the N-ammonia removal process in the extended WWTP Benchmark Simulation Model 1 (BSM1). It also performs better than a manual plant operator when disturbances affect the plant. Satisfactory experimental results show significant savings in a year of a working BSM1 plant. View Full-Text
Keywords: benchmark; energy saving; environmental impact; intelligent control; reinforcement learning; wastewater system benchmark; energy saving; environmental impact; intelligent control; reinforcement learning; wastewater system
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Hernández-del-Olmo, F.; Gaudioso, E.; Dormido, R.; Duro, N. Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning. Energies 2016, 9, 755.

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