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Record W2319761467 · doi:10.2166/ws.2011.046

A UASB bioreactor using silage as a carbon source to reduce sulfate

2011· article· en· W2319761467 on OpenAlexaff
Winton Li, Susan A. Baldwin

Bibliographic record

VenueWater Science & Technology Water Supply · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSulfateBioreactorSilageLeachateSulfideChemistryPulp and paper industryCarbon fibersEnvironmental chemistryEnvironmental scienceAnimal scienceWaste managementFood scienceBiologyMaterials science

Abstract

fetched live from OpenAlex

Low cost-treatment for sulfate removal is required in many areas where potable water is scarce. The biggest challenge in biological treatment is finding an abundant low or no-cost carbon source. This work demonstrated for the first time that leachate from the agricultural byproduct silage can be used in an upflow anaerobic sludge-bed bioreactor to reduce sulfate for on-farm water treatment. The reactor ran continuously for approximately one year with an average silage leachate feed COD concentration of 4,471 ± 857 mg L−1, and sulfate feed concentrations varying from 1,253 to 2,081 mg L−1. The maximum sulfate reduction rate (SRR) of 9.75 ± 0.23 mmol (L day)−1 was achieved at the high sulfate influent concentration and the amount of organics consumed was between 80–90%. Sulfide levels in the UASB bioreactor were consistently high for most of the experiment, averaging 516.6 ± 188.5 mg L−1. Interestingly, during the last month of operation when sulfide concentrations were highest the SRR continued to increase. It was estimated that 36% of the silage leachate carbon was used directly for sulfate reduction.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2011
Admission routes1
Has abstractyes

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