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Record W3092580839 · doi:10.20381/ruor-25420

Nitrifying Moving Bed Biofilm Reactors at Low Temperatures and Cold Shock Conditions: A Kinetic, Biofilm and Microbiome Study

2020· dissertation· en· W3092580839 on OpenAlexaboutno aff
Warsama Ahmed

Bibliographic record

VenueuO Research (University of Ottawa) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsBiofilmMicrobiomeKinetic energyMicrobiologyShock (circulatory)ChemistryEnvironmental chemistryEnvironmental scienceBiologyBacteriaPhysicsMedicineBioinformatics

Abstract

fetched live from OpenAlex

The nitrification process, the biologically mediated process of ammonia treatment in water resources recovery facilities (WRRF), remains the most common treatment process to mitigate the adverse effects of effluent ammonia discharges in surface water. However, it is well established that the temperature-sensitive process of nitrification remains hindered at low temperatures in conventional suspended growth technologies; specifically, passive treatment systems such as the lagoons, representing over 50% of Canadian treatment facilities in operation. As such, nitrification in lagoon facilities remains unreliable during the cold seasons with no nitrification occurring at 1°C. In contrast to suspended growth systems, attached growth technologies such as the moving bed biofilm reactors (MBBR) have recently been proven capable of achieving significant nitrification rates at temperatures as low as 1°C and are proposed as suitable upgrade systems to the common lagoon facility to reach year-long ammonia treatment targets. As such, the main objective of this research is to investigate and expand the current knowledge by investigating the key research questions lacking in the current literature on post-carbon, low temperature nitrifying MBBR systems. With this aim, a temperature-controlled study of attached growth nitrification kinetics was conducted to isolate the effects of low temperatures on nitrifying MBBR system performance down to 1°C. A removal rate of 98.44 ± 4.69 gN/m³d is identified as the 1°C intrinsic removal rate and the design removal rate for nitrifying MBBR systems at low temperatures. Considering this intrinsic rate at 1°C, an assessment of reactor efficiency at elevated TAN concentrations typical of non-combined sewer systems indicates that a two reactor in-series MBBR system configuration is recommended for retrofitting lagoon facilities connected to sanitary sewers. The study of the reactor performance to temperatures as low as 1°C demonstrates a non-linear decline in removal efficiency between 10°C and 1°C, with the existence of a kinetic threshold temperature delineated between 4°C and 2°C. As such, this delineated temperature range accounts for a significant decline in the performance of low carbon nitrifying MBBR systems during the onset of the cold seasons. This research identifies new recommended Arrhenius correction coefficient values taking into account this kinetic threshold temperature, with a coefficient of 1.049 being recommended above the kinetic threshold (≥4°C) and 1.149 below the threshold temperature at 1°C. Moreover, since the elapsed time to low temperature was identified as a key factor of attached growth nitrification kinetics, a modified theta model accounting for temperature and time is proposed in this research to accurately model the rate of nitrifying MBBR systems between 4°C and 1°C. Finally, with the severe adverse effects of sudden decreases in temperature, or cold shocks, on nitrification kinetics being previously demonstrated but not well understood, this research compares acclimatized and cold shocked MBBR reactors down to 1°C. The findings indicate 21% lower kinetics in the cold shocked reactor with reactor efficiencies never reaching those of the acclimatized reactor despite extended operation at 1°C. Thus, the research delineates the potentially lasting effects of extreme weather events such as cold air outbreaks and snowmelt periods on nitrifying MBBR system performance. On the other hand, these same findings demonstrate the resiliency of nitrifying MBBR reactors as nitrification was maintained within these systems despite being cold-shocked down from 10°C and 1°C. This study of attached growth kinetics was coupled with an investigation of the nitrifying biofilms, biomass, and microbiome responses to low temperatures and cold shock down to 1°C to provide an understanding of the changes occurring in these systems down to the cellular level. Comparisons of acclimatized and cold shocked nitrifying biofilms responses down to 1°C were characterized by increases in biofilm thickness, increases in biomass viability; and, greater shifts in microbiome communities occurring above 4°C in the acclimatized biofilm. Considering these observations, results also indicated a significant increase in nitrifiers per carrier above 4°C. As such, these findings suggested that the bulk of nitrifying biofilm adaptation to cold temperatures occurs above 4°C, a crucial adaptation phase in acclimatized systems. This adaptation phase is shown to be lacking in cold-shocked systems, with the cold shocked biofilm and microbiome demonstrating significant differences with the acclimatized systems’ biofilm and microbiome. This research was performed to answer the critical research questions relating to the design and operation of the post-carbon, low temperature nitrifying MBBR systems, with the first low temperature MBBR systems being scheduled to begin operation in the fall of 2020. This research expands the current knowledge on low temperature attached growth nitrification kinetics as well as cold shocked attached growth nitrification kinetics in MBBR systems down to 1°C. In addition, this research delineates the effects of low temperatures and cold shocks on the nitrifying MBBR system’s biofilms and their embedded cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.263
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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