Remaining Life Enhancement Technology: Lifting and Tilting Tall Vessels — A Coke Drum Case-Study (Without Cranes)
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".