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Autori Fiorentino Carmine ,Mancini Maurizio Luca , Luccarini Luca  Anno 2016
Tipologia Articolo Rivista internazionale con referaggio
Abstract The direct measurement of the nutrients largely contributes to understand how the quality of the information, coming from the
instruments, may improve the performances of the plants. The advanced knowledge acquired on the meaning of the signals gives
new possibilities to control the biological processes and estimate the operational state of the plant, improving the quality of the
effluent and keeping the operational costs as low as possible. Such information, furthermore, can be used to understand what
sensors to install and in what position of the plant.
Starting from an accurate literature update, this paper aims to propose some parameters, control sections and analytical methods
indispensable to monitoring and automatically control full-scale plants, with urban sewage coming from combined sewer systems.
In particular, a measurement campaign has been carried out in the WWTP of Bologna (Italy), a traditional continuous flow
Activated Sludge treatment without denitrification, during one year. Considering the influent in dry and rain conditions, the
behavior of the plant in different sections has been studied and characterized. Comparing the acquired data appears important to
monitor the BOD, COD and TSS concentrations at the primary sedimentation outlet, the input and the output sections of the
plant, as well as the TSS concentration in the aeration tank, measured using continuous optical methods. Finally, the study shows
the need for a monitoring system related with the sewage system dimensions and how Hydraulic Retention Time plays a key role
to identify and manage the inlet variations.
Referenza_Bibliografica Environmental Engineering & Management Journal (EEMJ)
Sep2016, Vol. 15 Issue 9, pp. 1981-1988
Unita di Ricerca X-LAB
LastUpdate 14/03/2017
Topic di ricerca collegati
Codice Topic di ricerca Descrizione
2.5.1 Trattamento reflui Civili
3.5 Sistemi supporto decisioni

impact factor Falso

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