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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Félix Hernández-del-Olmo & Elena Gaudioso & Raquel Dormido & Natividad Duro, 2016. "Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning," Energies, MDPI, vol. 9(9), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:755-:d:78308
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    References listed on IDEAS

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    1. Montse Meneses & Henry Concepción & Ramon Vilanova, 2016. "Joint Environmental and Economical Analysis of Wastewater Treatment Plants Control Strategies: A Benchmark Scenario Analysis," Sustainability, MDPI, vol. 8(4), pages 1-20, April.
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    Cited by:

    1. Mónica Vergara-Araya & Verena Hilgenfeldt & Di Peng & Heidrun Steinmetz & Jürgen Wiese, 2021. "Modelling to Lower Energy Consumption in a Large WWTP in China While Optimising Nitrogen Removal," Energies, MDPI, vol. 14(18), pages 1-24, September.

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