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Application of Neural Networks and Regression Modelling to Enable Environmental Regulatory Compliance and Energy Optimisation in a Sequencing Batch Reactor

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  • Shane Fox

    (School of Engineering, National University of Ireland Galway, H91TK33 Galway, Ireland
    Molloy Environmental Systems, Clara Rd, Coleraine, R35D956 Tullamore, Ireland)

  • James McDermott

    (School of Computer Science, National University of Ireland Galway, H91TK33 Galway, Ireland)

  • Edelle Doherty

    (School of Engineering, National University of Ireland Galway, H91TK33 Galway, Ireland
    Ryan Institute, National University of Ireland Galway, H91TK33 Galway, Ireland)

  • Ronan Cooney

    (School of Engineering, National University of Ireland Galway, H91TK33 Galway, Ireland
    Ryan Institute, National University of Ireland Galway, H91TK33 Galway, Ireland)

  • Eoghan Clifford

    (School of Engineering, National University of Ireland Galway, H91TK33 Galway, Ireland
    Ryan Institute, National University of Ireland Galway, H91TK33 Galway, Ireland)

Abstract

Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance.

Suggested Citation

  • Shane Fox & James McDermott & Edelle Doherty & Ronan Cooney & Eoghan Clifford, 2022. "Application of Neural Networks and Regression Modelling to Enable Environmental Regulatory Compliance and Energy Optimisation in a Sequencing Batch Reactor," Sustainability, MDPI, vol. 14(7), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4098-:d:783323
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    References listed on IDEAS

    as
    1. Maria Rosa di Cicco & Antonio Masiello & Antonio Spagnuolo & Carmela Vetromile & Laura Borea & Giuseppe Giannella & Manuela Iovinella & Carmine Lubritto, 2021. "Real-Time Monitoring and Static Data Analysis to Assess Energetic and Environmental Performances in the Wastewater Sector: A Case Study," Energies, MDPI, vol. 14(21), pages 1-16, October.
    2. Sebastian Lapuschkin & Stephan Wäldchen & Alexander Binder & Grégoire Montavon & Wojciech Samek & Klaus-Robert Müller, 2019. "Unmasking Clever Hans predictors and assessing what machines really learn," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
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