Dual substrate limitation modeling and implications for mainstream deammonification.
Substrate limitation occurs frequently in wastewater treatment and knowledge about microbial behavior at limiting conditions is essential for the use of biokinetic models in system design and optimization. Monod kinetics are well-accepted for modeling growth rates when a single substrate is limiting, but several models exist for treating two or more limiting substrates simultaneously. In this study three dual limitation models (multiplicative, minimum, and Bertolazzi) were compared based on experiments using nitrite-oxidizing bacteria (limited by dissolved oxygen and nitrite) and ANaerobic AMMonia-OXidizing bacteria or Aanammox (limited by ammonium and nitrite) within mixed liquor from deammonification pilots. A deterministic likelihood-based parameter estimation followed by Bayesian inference was used to estimate model-specific parameters. The minimum model outperformed the other two by a slight margin in three separate analyses. 1) Parameters estimated using the minimum model were closest to parameters estimated from single limitation batch tests. 2) Among simulations based on each model's own estimated parameters, the minimum model best described the experimental observations. 3) Among simulations based on parameters estimated from single limitation, the minimum model best described the experimental observations. The dual substrate model selected among the three studied can effect a 75% process performance variation based on simulations of a full-scale mainstream deammonification system.
The Catholic University of America, Washington, DC, USA; CH2M Hill, USA.
Heather A
HA
Stewart
DC Water, Washington, DC, USA.
Ahmed
A
Al-Omari
Hampton Roads Sanitation District, VA, USA.
Charles
C
Bott
DC Water, Washington, DC, USA.
Haydee
H
De Clippeleir
DC Water, Washington, DC, USA.
Chunyang
C
Su
Dynamita, Nyons, France.
Imre
I
Takacs
ARAconsult, Innsbruck, Austria.
Bernhard
B
Wett
The Catholic University of America, Washington, DC, USA. Electronic address: Massoudieh@CUA.edu.
Arash
A
Massoudieh
DC Water, Washington, DC, USA.
Sudhir
S
Murthy
D000641:Ammonia; D064751:Ammonium Compounds; D001499:Bayes Theorem; D019149:Bioreactors / Q000382:microbiology*; D016013:Likelihood Functions; D009573:Nitrites
D064751:Ammonium Compounds; D009573:Nitrites; D000641:Ammonia
Inverse modeling; Kinetics; Model discrimination; Shortcut nitrogen