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Record W2233736670 · doi:10.1115/ipc2010-31678

Designing CO2 Transmission Pipelines Without Crack Arrestors

2010· article· en· W2233736670 on OpenAlexaff
Graeme King, Satish Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsPipeline transportPipeline (software)Petroleum engineeringFossil fuelEngineeringMarine engineeringMechanical engineeringWaste management

Abstract

fetched live from OpenAlex

Masdar is developing several carbon capture projects from power plants, smelters, steel works, industrial facilities and oil and gas processing plants in Abu Dhabi in a phased series of projects. Captured CO2 will be transported in a new national CO2 pipeline network with a nominal capacity of 20×106 T/y to oil reservoirs where it will be injected for reservoir management and sequestration. Design of the pipeline network considered three primary factors in the selection of wall thickness and toughness, (a) steady and transient operating conditions, (b) prevention of longitudinal ductile fractures and (c) optimization of total project owning and operating costs. The paper explains how the three factors affect wall thickness and toughness. It sets out code requirements that must be satisfied when choosing wall thickness and gives details of how to calculate toughness to prevent propagation of long ductile fracture in CO2 pipelines. It then uses cost optimization to resolve contention between the different requirements and arrive at a safe and economical pipeline design. The design work selected a design pressure of 24.5 MPa, well above the critical point for CO2 and much higher than is normally seen in conventional oil and gas pipelines. Despite its high operating pressure, the proposed network will be one of the safest pipeline systems in the world today.

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: none
Teacher disagreement score0.775
Threshold uncertainty score0.525

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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