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Record W2300583515 · doi:10.1021/acs.iecr.5b02264

Effect of Ethanolamine, Ammonia, Acetic Acid, and Formic Acid on Two-Phase Flow Accelerated Corrosion in Steam–Water Cycles

2015· article· en· W2300583515 on OpenAlexaff
David H. Moed, Sarita Weerakul, D. H. Lister, Naravit Leaukosol, L.C. Rietveld, Arne Verliefde

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of New Brunswick
FundersElectric Power Research Institute
KeywordsFormic acidEthanolamineCorrosionAcetic acidAmmoniaChemistryInorganic chemistryVolumetric flow rateNuclear chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Effects of ethanolamine, ammonia, and acetic and formic acid on two-phase flow-accelerated corrosion were investigated in an experimental loop simulating the conditions found in a water-steam cycle. Results indicate that the effects of acetic acid and ethanolamine on the corrosion rate neutralize each other. The effect of acetic acid on the corrosion rate was most pronounced at the highest tested steam quality. A model simulation for liquid film pH at 90% steam quality suggests that at very high steam qualities the protection ethanolamine provides increases, while the protection provided by ammonia goes down. A linear relation between calculated liquid film pH and the measured corrosion rate was found for 24% steam quality within a pH range from 5.6 to 6.3. Formic acid was thermally unstable at the tested temperatures and had no effect on the 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.071
GPT teacher head0.360
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2015
Admission routes1
Has abstractyes

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