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Record W1972538865 · doi:10.1002/cjce.20454

Oxidation of sulfide ion in synthetic geothermal brines at carbon‐based anodes

2011· article· en· W1972538865 on OpenAlexafffundvenue
Jeff Hastie, Dorin Bejan, Nigel J. Bunce

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSulfidePolysulfideChemistryBrineAnodeInorganic chemistryHydrogen sulfidePetroleum cokeCokeSulfurOrganic chemistryElectrolyteElectrode

Abstract

fetched live from OpenAlex

Abstract Geothermal brines associated with natural gas extraction present an environmental problem because of the need to avoid the escape of toxic and odorous hydrogen sulfide. In this work, carbon‐based anodes (including graphite, granulated activated carbon (GAC), and industrial coke) were used in the electrochemical oxidation of sulfide ion in synthetic geothermal brines in both batch and flow cells, with a view to remediating this troublesome contaminant. Experiments were carried out in alkaline solution (to prevent volatilisation of H 2 S), in the absence or presence of added chloride ion and naphthenic acids (NAs). The product spread was variable due to the large number of competing reactions, including formation of polysulfide, oxidation to sulfate (either directly or via hypochlorination), sacrificial anode oxidation combined with sulfide trapping, and Kolbe oxidation of NAs combined with sulfide trapping. From the technological perspective, the product distribution is immaterial, because sour brines contain such high concentrations of inorganic and naphthenate salts that reinjection of the treated brine will always be required. The most efficient systems, on the basis of charge per mol sulfide remediated, employed packed bed reactors with ground coke or GAC anodes. In these reactors, the disappearance of sulfide ion was promoted, as expected, at low flow rate and high current. However, the packed beds were limited to low applied current, in order to avoid compromising the electrical conductivity of the anode bed by the production of gas (O 2 ).

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 score0.064
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.172
Teacher spread0.163 · 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

Citations14
Published2011
Admission routes3
Has abstractyes

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