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

Shane Fox, James McDermott, Edelle Doherty, Ronan Cooney and Eoghan Clifford
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Shane Fox: School of Engineering, National University of Ireland Galway, H91TK33 Galway, 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
Ronan Cooney: School of Engineering, National University of Ireland Galway, H91TK33 Galway, Ireland
Eoghan Clifford: School of Engineering, National University of Ireland Galway, H91TK33 Galway, Ireland

Sustainability, 2022, vol. 14, issue 7, 1-28

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.

Keywords: real-time control; neural network; soft sensor; regression; sequencing batch reactor (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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