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

Félix Hernández-del-Olmo, Elena Gaudioso, Raquel Dormido and Natividad Duro
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Félix Hernández-del-Olmo: Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, Spain
Elena Gaudioso: Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, Spain
Raquel Dormido: Department of Computer Sciences and Automatic Control, National Distance Education University (UNED), 28040 Madrid, Spain
Natividad Duro: Department of Computer Sciences and Automatic Control, National Distance Education University (UNED), 28040 Madrid, Spain

Energies, 2016, vol. 9, issue 9, 1-17

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.

Keywords: benchmark; energy saving; environmental impact; intelligent control; reinforcement learning; wastewater system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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