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Record W4409500926 · doi:10.5006/c2024-20618

Corrosion Fatigue Performance of Materials in Delayed Cokers and Coker Blowdown Piping System

2024· article· en· W4409500926 on OpenAlexaff
Haixia Guo, Millar Iverson, Simon Yuen, Sudeep Bohra, Liu Cao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsPipingBoiler blowdownCoker unitCorrosionMaterials scienceMetallurgyEngineeringMechanical engineeringCoke

Abstract

fetched live from OpenAlex

Abstract Very often the current body of knowledge and literature of fatigue performance in coke drums and blowdown piping system are linked to fatigue testing performed in air or in water. This paper summarizes the test results that were conducted using process fluids collected from a delayed coker. Both fatigue crack growth rate (FCGR) tests and stress/strain – cycle fatigue endurance tests in sour water at 200°F (93°C) or in a hot oil mix at 460°F (238°C), with gas mixture containing H2S were conducted. Carbon steel, Alloy 625 and their corresponding weld metal specimens were tested. The test results indicate remarkable impact of environment on fatigue behavior. Frequency scanning FCGR tests in sour water environment showed that FCGR increased with decreasing load frequency, particularly with high ΔK (32 ksi·√in or 35 MPa·√m). When ΔK was between 12 ksi·√in (13MPa·√m) and 36 ksi·√in (40 MPa·√m), FCGR of all specimens in sour water are higher than the mean + 2 SD (Standard Deviation) curve of carbon steel in air. Fatigue endurance tests in both sour water and hot oil environments showed various ranges of knock-down factors compared to test results in air. This testing program provides a better definition of the corrosion fatigue cracking mechanism of materials in delayed coker process. It also helps in the inspection planning of delayed coker equipment and in decisions on material selection.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.212

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 head0.215
Teacher spread0.205 · 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 designSimulation or modeling
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

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