MétaCan
Menu
Back to cohort
Record W2367012355

Effect of pH on performance of waste activated sludge fermentation coupling with denitrification

2014· article· en· W2367012355 on OpenAlexaff
B Wang

Bibliographic record

VenueJournal of Central South University(Science and Technology) · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsScience North
Fundersnot available
KeywordsDenitrificationChemistryActivated sludgeFermentationDissolutionPulp and paper industryEnvironmental chemistryWastewaterNitrogenFood scienceEnvironmental engineeringEnvironmental scienceOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

The effects of different pH(5, 7, 9, unregulated) on performance of waste activated sludge(WAS) coupling with denitrification were investigated to study sludge dissolution, substances release, sludge reduction and denitrification performance. In the batch experiment controlling temperature at(30±1) ℃, NO2--N was added into the system. The results indicate that when pH=5, the concentrations of carbohydrate is much higher than that at other pH values and the highest occurs at the 18th day, and it is 648.9 mg/L; when pH=5 and 9, the concentration of protein is higher, and the highest occurred when pH=9 at the 15th day, and it is 701.5 mg/L; when pH=5, PO4 3--P concentration is always higher than that at pH 9, however, NH4 +-N concentration is higher at pH=9. The system at different pH all performed sludge reduction. In addition, the order of denitrification performance(from good to bad) is pH=9, pH=7, pH unregulated and pH=5 according to NO2--N accumulation. In general, the optimal pH of waste activated sludge fermentation coupling with denitrification is 9.

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)0.000
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 score0.150
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.174
Teacher spread0.170 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Central South University(Science and Technology)Same topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207