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Sensitivity analysis for an elemental sulfur-based two-step denitrification model

Abstract A local sensitivity analysis was performed for a chemically synthesized elemental sulfur (S0)-based two-step denitrification model, accounting for nitrite (NO2−) accumulation, biomass growth and S0 hydrolysis. The sensitivity analysis was aimed at verifying the model stability, understanding the model structure and individuating the model parameters to be further optimized. The mass specific area of the sulfur particles (a*) and hydrolysis kinetic constant (k1) were identified as the dominant parameters on the model outputs, i.e. nitrate (NO3−), NO2− and sulfate (SO42−) concentrations, confirming that the microbially catalyzed S0 hydrolysis is the rate-limiting step during S0-driven denitrification. Additionally, the maximum growth rates of the denitrifying biomass on NO3− and NO2− were detected as the most sensitive kinetic parameters.
Elemental sulfur, Environmental Engineering, Biological surface-based hydrolysis; Elemental sulfur; Mathematical modeling; Sensitivity analysis; Two-step autotrophic denitrification; Environmental Engineering; Water Science and Technology, Two-step autotrophic denitrification, Bioreactors, European Joint Doctorates, European Commission, Knowmad Institut, Biological surface-based hydrolysis, Nitrites, Netherlands, Water Science and Technology, Aurora Universities Network, EC, Nitrates, H2020, Energy Research, Denitrification, Mathematical modeling, Sensitivity analysis, Sulfur
Elemental sulfur, Environmental Engineering, Biological surface-based hydrolysis; Elemental sulfur; Mathematical modeling; Sensitivity analysis; Two-step autotrophic denitrification; Environmental Engineering; Water Science and Technology, Two-step autotrophic denitrification, Bioreactors, European Joint Doctorates, European Commission, Knowmad Institut, Biological surface-based hydrolysis, Nitrites, Netherlands, Water Science and Technology, Aurora Universities Network, EC, Nitrates, H2020, Energy Research, Denitrification, Mathematical modeling, Sensitivity analysis, Sulfur
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