Real-Time Monitoring and Static Data Analysis to Assess Energetic and Environmental Performances in the Wastewater Sector: A Case Study
| Autor: | Laura Borea, Manuela Iovinella, Antonio Masiello, Carmine Lubritto, Maria Rosa di Cicco, Carmela Vetromile, Antonio Spagnuolo, Giuseppe Giannella |
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| Přispěvatelé: | Di Cicco, M. R., Masiello, A., Spagnuolo, A., Vetromile, C., Borea, L., Giannella, G., Iovinella, M., Lubritto, C. |
| Rok vydání: | 2021 |
| Předmět: |
Normalization (statistics)
Technology Control and Optimization PH Energy Engineering and Power Technology sensors Total suspended solid Electrical and Electronic Engineering Process engineering Dynamic monitoring Engineering (miscellaneous) dynamic monitoring load factors KPI pH temperature total suspended solids urban wastewater Sensor Resource recovery Total suspended solids Renewable Energy Sustainability and the Environment business.industry Dynamic data Urban wastewater Temperature Process (computing) Building and Construction Load factor Wastewater Environmental science Sewage treatment Performance indicator business Energy (miscellaneous) |
| Zdroj: | Energies, Vol 14, Iss 6948, p 6948 (2021) Energies; Volume 14; Issue 21; Pages: 6948 |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en14216948 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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