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A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs

Author

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  • Bárbara de Matos

    (Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
    These authors contributed equally to this work.)

  • Rodrigo Salles

    (Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
    These authors contributed equally to this work.)

  • Jérôme Mendes

    (Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal)

  • Joana R. Gouveia

    (INEGI-Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Campus da FEUP, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal)

  • António J. Baptista

    (INEGI-Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Campus da FEUP, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal)

  • Pedro Moura

    (Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal)

Abstract

Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Suggested Citation

  • Bárbara de Matos & Rodrigo Salles & Jérôme Mendes & Joana R. Gouveia & António J. Baptista & Pedro Moura, 2022. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:173-:d:1018837
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    References listed on IDEAS

    as
    1. Chun-Ming Xu & Jia-Shuai Zhang & Ling-Qiang Kong & Xue-Bo Jin & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2022. "Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    2. S. Revollar & R. Vilanova & P. Vega & M. Francisco & M. Meneses, 2020. "Wastewater Treatment Plant Operation: Simple Control Schemes with a Holistic Perspective," Sustainability, MDPI, vol. 12(3), pages 1-28, January.
    3. Yu, Ruey-Fang & Chen, Ho-Wen & Cheng, Wen-Po & Shen, Yu-Chiu, 2008. "Dynamic control of disinfection for wastewater reuse applying ORP/pH monitoring and artificial neural networks," Resources, Conservation & Recycling, Elsevier, vol. 52(8), pages 1015-1021.
    4. ZhenHua Li & ZhiHong Zou & LiPing Wang, 2019. "Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, July.
    5. Longo, S. & Mauricio-Iglesias, M. & Soares, A. & Campo, P. & Fatone, F. & Eusebi, A.L. & Akkersdijk, E. & Stefani, L. & Hospido, A., 2019. "ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants," Applied Energy, Elsevier, vol. 242(C), pages 897-910.
    6. Jorge Pereira & Jérôme Mendes & Jorge S. S. Júnior & Carlos Viegas & João Ruivo Paulo, 2022. "A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
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