A local sensitivity analysis was performed for a S0-driven two-step denitrification model, accounting for NO2 - accumulation, biomass growth and S0 solubilization. The model sensitivity was aimed at verifying the model stability, understanding the identifiability of the model structure and evaluating the model parameters to be further optimized. The sensitivity analysis identified the mass specific area of the sulfur particles (a*) and hydrolysis kinetic constant (k1) as the dominant parameters. Additionally, the maximum growth rate of the denitrifying biomass on NO3 - (μmax 2,3) and NO2 - (μmax 2,4) were detected as the most sensitive kinetic parameters. Further calibration would be performed for the sensitive model parameters to optimize the quality of the model.
A sensitivity analysis for sulfur-driven two-step denitrification model / Anastasiia, Kostrytsia; Papirio, Stefano; Mattei, MARIA ROSARIA; Frunzo, Luigi; Piet N. L., Lens; Giovanni, Esposito; Esposito, Giovanni. - (2017). (Intervento presentato al convegno S2SMALL International IWA Conference on sustainable solutions for small water and wastewater treatment systems tenutosi a Nantes, France nel 22-26 ottobre 2017).
A sensitivity analysis for sulfur-driven two-step denitrification model
PAPIRIO, Stefano;MATTEI, MARIA ROSARIA;FRUNZO, LUIGI;ESPOSITO, GIOVANNI
2017
Abstract
A local sensitivity analysis was performed for a S0-driven two-step denitrification model, accounting for NO2 - accumulation, biomass growth and S0 solubilization. The model sensitivity was aimed at verifying the model stability, understanding the identifiability of the model structure and evaluating the model parameters to be further optimized. The sensitivity analysis identified the mass specific area of the sulfur particles (a*) and hydrolysis kinetic constant (k1) as the dominant parameters. Additionally, the maximum growth rate of the denitrifying biomass on NO3 - (μmax 2,3) and NO2 - (μmax 2,4) were detected as the most sensitive kinetic parameters. Further calibration would be performed for the sensitive model parameters to optimize the quality of the model.File | Dimensione | Formato | |
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