MétaCan
Menu
Back to cohort
Record W2767190818 · doi:10.1115/pvp2017-65098

Remaining Life Enhancement Technology: Lifting and Tilting Tall Vessels — A Coke Drum Case-Study (Without Cranes)

2017· article· en· W2767190818 on OpenAlexaffabout
A. Kaye

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsCanadian Natural Resources
Fundersnot available
KeywordsDrumCokeEngineeringShell (structure)Investment (military)Mechanical engineeringManufacturing engineeringWaste management

Abstract

fetched live from OpenAlex

Throughout the refining industry, there is a need to increase the return on investment of aging assets. Remaining life technology application and development is widely adopted to increase value from existing infrastructure and equipment. In this paper, an innovative way of continuing to utilize compromised vessels was creatively pursued. The technique and principles can be applied to vessels or equipment that is known to have shape deficiencies without having to replace sections, components, whole shells, drums, towers or casings. In this way, the costly rebuild work and greater loss of production can be avoided. Replacement was circumvented in a Delayed Coking Unit (DCU) for a Canadian oilsands upgrader. In this case study, principles were taken from the building transportation and moving industry and applied to lifting and tilting of 145ft (44.2m) high coke drums. The ability to tilt and re-align a vessel of 14 storey (equivalent height) thin shell coke drum was believed to be possible and was subsequently performed successfully, at this location, multiple times. These were the largest coke drums in the world (at the date of their fabrication in 2006). The design and engineering issues are discussed in detail, including the techniques and analysis, stability, protection against buckling and finally; inspection and verification.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
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 studies0.0010.000
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 head0.016
GPT teacher head0.254
Teacher spread0.239 · 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 designObservational
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
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicMarine and Offshore Engineering StudiesFrench-language works237,207