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Record W1969920515 · doi:10.1021/ie9007453

Corrosion Behavior of Carbon Steel in the Monoethanolamine−H<sub>2</sub>O−CO<sub>2</sub>−O<sub>2</sub>−SO<sub>2</sub> System

2009· article· en· W1969920515 on OpenAlexafffund
Nattawan Kladkaew, Raphael Idem, Paitoon Tontiwachwuthikul, Chintana Saiwan

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeThailand Research Fund
KeywordsCorrosionCarbon steelFlue gasCarbonic acidOxygenChemistrySolubilityInorganic chemistryHydrogenHydroniumCarbon fibersCarbon dioxideMetallurgyIonMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of operating parameters on the corrosion of carbon steel in the monoethanolamine (MEA)−H 2 O−CO 2 −O 2 −SO 2 system were investigated using two different corrosion measurement techniques. The corrosion studies were conducted using a 273A potentiostat using MEA, O 2, and SO 2 concentrations and CO 2 loading in the ranges of 1−7 kmol/m 3, 0−100%, 0−204 ppm, and 0−0.5 mol CO 2 /mol MEA, respectively, at corrosion temperatures in the range of 303−353 K. The experimental results showed, for the first time, that a higher concentration of SO 2 in a simulated flue gas stream induces a higher corrosion rate essentially because of the increase in the hydrogen ion concentration generated by reactions of SO 2 and H 2 O as well as SO 2, O 2, and H 2 O. Also, an increase in oxygen concentration in the simulated flue gas stream causes a higher corrosion rate due to the increasing solubility of oxygen and, in turn, a higher amount of dissolved oxygen in the liquid phase. The results further show that an increase in the concentrations of MEA, O 2, and SO 2 as well as CO 2 loading will cause the generation of higher amounts of hydrogen or hydronium ions, as well as carbonic acid and bicarbonate ions, and this is what leads to a higher corrosion rate.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
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 score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.004
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 head0.047
GPT teacher head0.295
Teacher spread0.248 · 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

Citations37
Published2009
Admission routes2
Has abstractyes

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