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Sequential Anaerobic/Aerobic Digestion for Enhanced Carbon/Nitrogen Removal and Cake Odor Reduction

2016· article· en· W2425407157 on OpenAlexaffabout
Muneer Ahmad, Marco Abel Denee, Hao Jiang, Çiğdem Eskicioğlu, Paul Kadota, Theresa Gregonia

Bibliographic record

VenueWater Environment Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsAnaerobic digestionEffluentAerobic digestionDigestion (alchemy)Pulp and paper industryWastewaterSewage treatmentChemistryAnaerobic exerciseFecal coliformWaste managementOdorNutrientEnvironmental scienceEnvironmental engineeringMethaneBiologyWater qualityChromatographyEcology

Abstract

fetched live from OpenAlex

Anaerobic digestion (AD) has been proven to be an effective process for the treatment of wastewater sludge. However, it produces high levels of ammonia in the digester effluent, which may jeopardize meeting stringent nutrient discharge limits. In this study, the effect of a sequential anaerobic/aerobic (AN/AERO) digestion and a single-stage conventional AN digestion (as control) was investigated on mixed (primary + secondary) sludge generated by the Annacis Island wastewater treatment plant (WWTP) (BC, Canada). An overall sludge retention time (SRT) of 22.5 days under three different scenarios was chosen based on the current operational SRT of the digesters at the Annacis Island WWTP. The steady state results have shown that sequential AN/AERO digestion configurations achieved up to 11% higher volatile solids (VS) removal and 72% lower ammonia generation over single-stage conventional AN digestion. Furthermore, sequential AN/AERO system also showed enhanced dewaterability, improved fecal coliform destruction and reduced digested cake odors over control digesters.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.033
GPT teacher head0.273
Teacher spread0.240 · 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 teacher head, not a consensus.

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".

Quick stats

Citations10
Published2016
Admission routes2
Has abstractyes

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