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Modelling biological nitrogen and phosphorus removal with soluble microbial products (SMP) production-degradation processes

  • Autori: Cosenza, A.; Mannina, G.; Neuwman, M.; Viviani, G.; Vanrolleghem, P.
  • Anno di pubblicazione: 2011
  • Tipologia: Proceedings (TIPOLOGIA NON ATTIVA)
  • Parole Chiave: ASM2d-SMP; MBR modelling; membrane fouling; model calibration; nitrogen phosphorus removal

Abstract

Over the last two decades, Membrane Bioreactors (MBR) are increasingly used for wastewater treatment. Mathematical modelling of MBR systems has played a key role in order to better explain the effect of their peculiarities. Indeed, several MBR models have been presented in literature in order to improve the knowledge on MBR systems: biological models, hybrid models which include soluble microbial product (SMP) modelling, physical models able to describe the membrane fouling and integrated models which couple the hybrid models with the physical ones. However, among the existing MBR models only few integrated models have been developed which take into account the existing relationship between fouling and the biological processes. Also, with respect to modelling of biological phosphorus removal in MBR systems, due to the complexity of the process, practical use of the models is still limited. There is a vast knowledge (and consequently a vast amount of data) on nutrient removal for conventional activated sludge (CAS) systems but only limited information on phosphorus removal for MBRs. Moreover, calibration of these complex integrated models still remains the main bottleneck to their employment. The paper presents an integrated mathematical model able to simultaneously describe biological nutrient removal, the SMP formation/degradation and the physical removal of organics. The model has been calibrated by using data collected in a UCT-MBR pilot plant, built at the Palermo WWTP and fed with real wastewater, applying an innovative calibration protocol. The calibrated model provides acceptable correspondence with experimental data.