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Modelling simultaneous anaerobic methane and ammonium removal in a granular sludge reactor
M-K H Winkler1, K F Ettwig2, T P W Vannecke3
1Department of Biosystems Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA.
Water Research
|February 21, 2015
Summary
This study models anaerobic nitrogen removal, finding that nitrite and methane oxidizing bacteria (n-damo) survival depends on influent ratios. Process optimization is key for efficient coexistence and nutrient removal.
Area of Science:
- Environmental microbiology
- Wastewater treatment technologies
- Biogeochemical processes
Background:
- Anaerobic nitrogen removal offers energy and cost benefits over conventional systems.
- Investigating the coexistence of n-damo and anammox bacteria is crucial for optimizing these processes.
Purpose of the Study:
- To model the coexistence of n-damo and anammox bacteria in a single granule.
- To evaluate the influence of process operation on their survival and removal efficiencies.
Main Methods:
- Experimental measurement of nitrite and methane affinity constants for n-damo bacteria.
- Biomass yield derivation using experimental data and thermodynamic analysis.
- Mathematical modeling and simulations to assess bacterial competition and removal rates.
Main Results:
- N-damo survival is sensitive to the nitrite/ammonium influent ratio; excess ammonium leads to n-damo outcompetition.
- Low biomass concentration favors anammox over n-damo; high biomass loading causes substrate competition and oscillating removal rates.
- Smaller granules enhance simultaneous ammonium and methane removal efficiencies.
Conclusions:
- Process operation, particularly the influent ratio and biomass concentration, significantly impacts n-damo and anammox coexistence.
- Simultaneous anaerobic methane and ammonium removal can reduce greenhouse gas emissions but may increase aeration costs.
- Practical implementation requires careful consideration of operational parameters and economic factors.
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