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Record W2029659839 · doi:10.1115/pvp2008-61383

Layered Vessel Construction for Hydroprocessing Reactors

2008· article· en· W2029659839 on OpenAlexaff
Jan T. Andersson, Radoslav Stefanovic, Steven Kristensen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsHusky Energy (Canada)
Fundersnot available
KeywordsNozzleShell (structure)Reactor designMaterials sciencePressure vesselService lifeReactor pressure vesselMechanical engineeringProcess engineeringEngineeringNuclear engineering

Abstract

fetched live from OpenAlex

The demand for hydroprocessing reactors has increased dramatically in recent years. In addition, reactor size and thickness has also increased. New materials with higher allowable stresses have been introduced. Lead times and prices for vessels have increased for many reasons, one of which is because of the limited number of mills that are capable of producing advanced materials for the higher thicknesses required. For many years, layered design was successfully used for high pressure vessels including hydroprocessing reactors, including some that have 40 years of service history. Yet, recent history shows that hydroprocessing reactors are exclusively built using solid wall design. This paper discusses the advantages and disadvantages of using layered construction for hydroprocessing reactors. Consideration is given to mechanical properties, venting of diffused hydrogen, thermal conductivity differences between the layered parts and solid sections, and non-destructive examination. Specific design issues related to bed support design, nozzle to shell, and head to shell connection construction are discussed. As a part of the evaluation, finite element analysis has been recommended to study critical areas for layered design assessment. An industry survey of layered reactor service is suggested as well.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.300

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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 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
Published2008
Admission routes1
Has abstractyes

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