Predicting aeration time and nitrite accumulation rate variations for Partial Nitritation: A model incorporating...

This study aimed to develop a two-step nitrification model to predict variations in aeration time and nitrite accumulation rate (NAR) under fluctuating operational conditions in mainstream partial nitritation (PN) processes. Lab-scale sequencing batch reactors (SBRs) were used to evaluate the ammonia oxidation rate (AOR) and nitrite oxidation rate (NOR) under different solids retention times (SRT) (10, 15, 20, 30, and 50 days) and total volumetric nitrogen loadings (TVNL) (20-60 mg N/L per cycle). A static model was developed to predict consistent AOR and NOR values in the steady state, whereas a dynamic model was established to capture the growth dynamics of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) under unsteady-state conditions. The static model accurately predicted the AOR, NOR, and aeration time at steady state. The dynamic model quantified the relationship between specific growth rates (μ) and food-to-microorganism ratios (F/M) through exponential fitting, successfully capturing AOB and NOB growth dynamics. Validation experiments (SRT = 10 d, TVNL = 60 mg/L per cycle) demonstrated the ability of the dynamic model to predict trends in NAR and aeration time accurately. This study emphasizes the importance of accurately modeling AOR and NOR variations to predict aeration time and NAR, thereby providing valuable insights for aeration control and precise management of AOB and NOB populations in mainstream PN processes.