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Algal-bacterial shortcut nitrogen removal model with seasonal light variations
by
Iman Shayan, Sahand
, van der Steen, Peter
, Zhang, Qiong
, Youssef, Steve
, Ergas, Sarina J.
in
Aeration
/ Algae
/ Ammonia
/ Anaerobic treatment
/ Bacteria
/ Batch reactors
/ Biomass
/ Bioreactors
/ Bioreactors - microbiology
/ Capital costs
/ Carbon
/ Denitrification
/ Digesters
/ Efficiency
/ full-scale design
/ Hydraulic retention time
/ Inorganic carbon
/ Laboratories
/ Light
/ Luminous intensity
/ mathematical model
/ Mathematical models
/ Natural lighting
/ Nitrogen
/ Nitrogen - analysis
/ Nitrogen removal
/ photo-sequencing batch reactor
/ Retention time
/ Seasons
/ Sequences
/ Sequencing batch reactor
/ shortcut nitrogen removal
/ sidestream wastewater
/ Summer
/ Wastewater
/ Wastewater treatment
/ Wastewater treatment plants
/ Water treatment plants
2024
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Algal-bacterial shortcut nitrogen removal model with seasonal light variations
by
Iman Shayan, Sahand
, van der Steen, Peter
, Zhang, Qiong
, Youssef, Steve
, Ergas, Sarina J.
in
Aeration
/ Algae
/ Ammonia
/ Anaerobic treatment
/ Bacteria
/ Batch reactors
/ Biomass
/ Bioreactors
/ Bioreactors - microbiology
/ Capital costs
/ Carbon
/ Denitrification
/ Digesters
/ Efficiency
/ full-scale design
/ Hydraulic retention time
/ Inorganic carbon
/ Laboratories
/ Light
/ Luminous intensity
/ mathematical model
/ Mathematical models
/ Natural lighting
/ Nitrogen
/ Nitrogen - analysis
/ Nitrogen removal
/ photo-sequencing batch reactor
/ Retention time
/ Seasons
/ Sequences
/ Sequencing batch reactor
/ shortcut nitrogen removal
/ sidestream wastewater
/ Summer
/ Wastewater
/ Wastewater treatment
/ Wastewater treatment plants
/ Water treatment plants
2024
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Algal-bacterial shortcut nitrogen removal model with seasonal light variations
by
Iman Shayan, Sahand
, van der Steen, Peter
, Zhang, Qiong
, Youssef, Steve
, Ergas, Sarina J.
in
Aeration
/ Algae
/ Ammonia
/ Anaerobic treatment
/ Bacteria
/ Batch reactors
/ Biomass
/ Bioreactors
/ Bioreactors - microbiology
/ Capital costs
/ Carbon
/ Denitrification
/ Digesters
/ Efficiency
/ full-scale design
/ Hydraulic retention time
/ Inorganic carbon
/ Laboratories
/ Light
/ Luminous intensity
/ mathematical model
/ Mathematical models
/ Natural lighting
/ Nitrogen
/ Nitrogen - analysis
/ Nitrogen removal
/ photo-sequencing batch reactor
/ Retention time
/ Seasons
/ Sequences
/ Sequencing batch reactor
/ shortcut nitrogen removal
/ sidestream wastewater
/ Summer
/ Wastewater
/ Wastewater treatment
/ Wastewater treatment plants
/ Water treatment plants
2024
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Algal-bacterial shortcut nitrogen removal model with seasonal light variations
Journal Article
Algal-bacterial shortcut nitrogen removal model with seasonal light variations
2024
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Overview
The algal–bacterial shortcut nitrogen removal (ABSNR) process can be used to treat high ammonia strength wastewaters without external aeration. However, prior algal–bacterial SNR studies have been conducted under fixed light/dark periods that were not representative of natural light conditions. In this study, laboratory-scale photo-sequencing batch reactors (PSBRs) were used to treat anaerobic digester sidestream under varying light intensities that mimicked summer and winter conditions in Tampa, FL, USA. A dynamic mathematical model was developed for the ABSNR process, which was calibrated and validated using data sets from the laboratory PSBRs. The model elucidated the dynamics of algal and bacterial biomass growth under natural illumination conditions as well as transformation processes for nitrogen species, oxygen, organic and inorganic carbon. A full-scale PSBR with a 1.2 m depth, a 6-day hydraulic retention time (HRT) and a 10-day solids retention time (SRT) was simulated for treatment of anaerobic digester sidestream. The full-scale PSBR could achieve >90% ammonia removal, significantly reducing the nitrogen load to the mainstream wastewater treatment plant (WWTP). The dynamic simulation showed that ABSNR process can help wastewater treatment facilities meet stringent nitrogen removal standards with low energy inputs.
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