MétaCan
Menu
Back to cohort
Record W4409500604 · doi:10.5006/c2024-20809

Corrosion of Cr-Alloyed Steels in Supercritical CO2-Saturated Brine Water

2024· article· en· W4409500604 on OpenAlexaff
Alexander Gross, Haofei Sun, Jing Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSupercritical fluidBrineCorrosionMaterials scienceMetallurgyEnvironmental sciencePetroleum engineeringGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract In the context of urgent efforts to reduce global carbon emissions, carbon capture, utilization, and storage (CCUS) has gained prominence. Tubing and casing steels are vital materials of construction (MOC) in CCUS systems, especially in supercritical CO2 (s-CO2) environments. However, the understanding of the corrosion behavior of these steels under s-CO2 conditions is limited, which presents integrity challenges. Immersion experiments were conducted with steels containing varying chromium (Cr) levels (0.5 wt% to 16.5 wt%), including P110, P91, and SS 316. These steels were exposed to s-CO2-saturated brine water (3.5 wt% NaCl) at 50 °C and 10 MPa for durations of 10 and 100 hours. Corrosion rates were determined via weight loss measurements, and corrosion products were analyzed with SEM and XRD. P110 displayed the highest corrosion rates (15.7 mm/y at 10 hours, 2.9 mm/y at 100 hours) but developed a thick FeCO3 layer after 100 hours. In contrast, P91 and SS 316 exhibited lower corrosion rates after 100 hours (0.04 mm/y and < 0.01 mm/y, respectively). The influence of Cr content and exposure duration on the corrosion behavior of these steels were discussed. All steels exhibited decreasing corrosion rates over time, primarily due to oxide formation, enhancing the understanding of casing and tubing steel corrosion in s-CO2 environments.

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 categoriesInsufficient payload (model declined to judge)
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 score0.026
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
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicSubcritical and Supercritical Water ProcessesFrench-language works237,207