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Real-Time Monitoring and Static Data Analysis to Assess Energetic and Environmental Performances in the Wastewater Sector: A Case Study

Author

Listed:
  • Maria Rosa di Cicco

    (Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy)

  • Antonio Masiello

    (Energreenup srl, 81051 Pietramelara, Italy)

  • Antonio Spagnuolo

    (Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
    Energreenup srl, 81051 Pietramelara, Italy)

  • Carmela Vetromile

    (Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
    Energreenup srl, 81051 Pietramelara, Italy)

  • Laura Borea

    (ASIS Reti e Impianti SpA, 84043 Agropoli, Italy)

  • Giuseppe Giannella

    (ASIS Reti e Impianti SpA, 84043 Agropoli, Italy)

  • Manuela Iovinella

    (Department of Biology, University of York, York YO10 5DD, UK)

  • Carmine Lubritto

    (Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
    National Institute of Nuclear Physics—Naples Department, 80126 Napoli, Italy)

Abstract

Real-time monitoring of energetic-environmental parameters in wastewater treatment plants enables big-data analysis for a true representation of the operating condition of a system, being still frequently mismanaged through policies based on the analysis of static data (energy billing, periodic chemical–physical analysis of wastewater). Here we discuss the results of monitoring activities based on both offline (“static”) data on the main process variables, and on-line (“dynamic”) data collected through a monitoring system for energetic-environmental parameters (dissolved oxygen, wastewater pH and temperature, TSS intake and output). Static-data analysis relied on a description model that employed statistical normalization techniques (KPIs, operational indicators). Dynamic data were statistically processed to explore possible correlations between energetic-environmental parameters, establishing comparisons with static data. Overall, the system efficiently fulfilled its functions, although it was undersized compared to the organic and hydraulic load it received. From the dynamic-data analysis, no correlation emerged between energy usage of the facility and dissolved oxygen content of the wastewater, whereas the TSS removal efficiency determined through static measurements was found to be underestimated. Finally, using probes allowed to characterize the pattern of pH and temperature values of the wastewater, which represent valuable physiological data for innovative and sustainable resource recovery technologies involving microorganisms.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6948-:d:662241
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    References listed on IDEAS

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    1. 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.
    2. Stefano Longo, Mona Chitnis, Miguel Mauricio-Iglesias, Almudena Hospido, 2020. "Transient and Persistent Energy Efficiency in the Wastewater Sector based on Economic Foundations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6), pages 233-254.
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    Cited by:

    1. 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.

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