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Record W2019327479 · doi:10.2166/wst.2007.369

Anaerobic pond treatment of wastewater containing sulphate

2007· article· en· W2019327479 on OpenAlexaff
B.K. Rajbhandari, Ajit P. Annachhatre

Bibliographic record

VenueWater Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOrganic matterWastewaterAnaerobic exercisePulp and paper industryChemistrySulfate-reducing bacteriaEnvironmental chemistrySubstrate (aquarium)MethaneEnvironmental scienceSulfateEnvironmental engineeringBiologyEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Anaerobic ponds are usually used for treatment of industrial and agricultural wastes which contain high organic matter and sulphate. Competition for substrate between sulphate reducing bacteria and methane producing archaea, and the inhibitory effects of sulphide produced from microbial sulphate reduction reported in the literature varied considerably. In this research, a laboratory scale column-in-series anaerobic pond reactor, consisting of five cylindrical columns of acrylic tubes, was operated to evaluate the effect of COD and sulphate ratio on pond performance treating wastewater containing high organic matter and sulphate from a tapioca starch industry. The result depicted that no adverse effect of COD:SO4 ratios between 5 and 20 on overall COD removal performance of anaerobic pond operated with organic loading rate (OLR) of 150 to 600 g COD/m3d. Sulphate reducing bacteria could out-compete methane producing archaea for the same substrate at COD:SO4 ratio equal to or lower than 5 and OLR greater than 300 g COD/m3d. Sulphide inhibition was not observed on overall performance of pond up to an influent sulphate concentration of 650 mg/L.

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 score0.010
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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
Published2007
Admission routes1
Has abstractyes

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