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Record W2333751064 · doi:10.2166/wst.2011.328

Retrofitting conventional primary clarifiers to activated primary clarifiers to enhance nutrient removal and energy conservation in WWTPs in Beijing, China

2011· article· en· W2333751064 on OpenAlexaff
Jiawei Wang, Tianzhu Zhang, Jining Chen, Zhirong Hu

Bibliographic record

VenueWater Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnviroSim (Canada)
Fundersnot available
KeywordsClarifierEffluentActivated sludgeEnvironmental scienceNutrientWastewaterSewage treatmentEnvironmental engineeringWaste managementBiomass (ecology)Pulp and paper industryPhosphorusRetrofittingChemistryEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Biological nutrient removal requires sufficient carbon source. Meanwhile, the removal of organic matter in wastewater requires energy consumption in the aeration tank. Carbon source for nutrient removal in most wastewater treatment plants with conventional primary clarifier (CPC) is generally insufficient in China. In order to increase carbon source and to save energy, a part of the CPC may be retrofitted as an activated primary clarifier (APC). In this paper, a pilot scale experiment was conducted to examine the performance of primary sludge fermentation and its effect on nitrogen and phosphorus removal. Results show that the primary sludge fermentation in APC has produced a similar VFA/TP ratio but a higher BOD5/TN ratio compared with those in the CPC effluent, and the TN concentrations in the secondary effluent are at 8.0, 10.8, and 17.4 mg/L, while TP is at 0.45, 1.10, and 2.28 mg/L when the pilot test system was fed with (1) the APC effluent, (2) 50% from the APC effluent and 50% from the CPC effluent, and (3) the CPC effluent, respectively. Results also indicate that the BOD5/TN ratio is a more sensitive factor than the VFA/TP ratio for nutrient removal and energy conservation for the APC fermentation.

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 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 score0.022
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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.

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".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

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