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Record W2595047803 · doi:10.5006/c2011-11092

Effects of Reaction Kinetics of H2S, CO2 and O2 on the Formation of Black Powder in Sales Gas Pipelines

2011· article· en· W2595047803 on OpenAlexaff
Robin Susilo, Boyd Davis, Abdelmounam Sherik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsKingston Process Metallurgy (Canada)
Fundersnot available
KeywordsKineticsPipeline transportMaterials scienceChemical engineeringChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Carbon dioxide (CO2), hydrogen sulphide (H2S) and oxygen (O2) gases dissolve in water condensate on carbon steel pipelines that react with the walls to form iron carbonate, iron sulphides, and iron oxides as the corrosion products, respectively. It is unknown whether these gases react individually or if there is a competition between them leading to a kinetically favorable reaction. The reaction rate is governed by the concentration of gases dissolved in water which is in turn controlled by the gas composition, diffusivity, solubility, and the intrinsic kinetics. The reaction rate and products when fine iron powder (-325 mesh) suspended in water exposed to dry acid gas (CO2 and H2S) and exposed to humid acid gas are studied. Our findings show that water immersion approach for corrosion study without mixing has a mass transfer resistance that slows down the reaction rate significantly. This may not be observed in actual gas pipelines as the pipeline is exposed to thin water film only and the turbulence created by the gas flow induces mixing. Iron sulphide and carbonate formation are found to depend on the CO2/H2S ratio and time. Initially, the formation of iron carbonate is more dominant than iron sulphide. Higher the CO2/H2S ratio leads to the higher iron carbonate formation than iron sulphide. Iron sulphide becomes more dominant in a longer time scale for all CO2/H2S ratios. Iron sulphide is oxidized to elemental sulphur when O2 is present.

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 score0.027
Threshold uncertainty score0.113

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 head0.028
GPT teacher head0.240
Teacher spread0.213 · 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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