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Record W2007646203 · doi:10.4043/21426-ms

Floating Regasification Terminals - Selection And Marinisation Of Regasification Equipment For Offshore Use

2011· article· en· W2007646203 on OpenAlexaff
Holger Kelle, Yee-jun Wong, Jorg Schlatt

Bibliographic record

VenueOffshore Technology Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsOperabilitySubmarine pipelineContext (archaeology)EngineeringEnvironmental scienceMarine engineeringReliability engineering

Abstract

fetched live from OpenAlex

Abstract This paper presents an overview of existing regasification technologies, discusses key marinisation issues and their adaptability and design for offshore use as well as consideration of the environmental impact. To assist in the development of a high level CAPEX critical for early project feasibility studies, key cost drivers associated with marinisation will also be discussed. In the context of rising global energy demand and lack of space for shore-based regasification terminals, the use of floating regasification developments are on the rise. The success of floating regasification terminals lies in the design of the regasification and process equipment, which needs to maintain high availability at the required regasification rate whilst under the influence of offshore environments and ship motions. Furthermore, for floating regasification facilities that are permanently moored offshore, the regasification equipment will also need to withstand the harshest marine environments. The regasification equipment therefore needs to be designed for offshore operations and survivability. Marinisation is a common approach used in the design of offshore floating production facilities (FPSOs) and extending this concept to floating regasification involves adapting existing and proven shore/land based regasification equipment for the offshore environment. In addition to ensuring operability, requirements for high availability influence the equipment layout and placement on deck to minimize repair downtimes. This paper is targeted towards projects or developments centered around permanently moored, offshore floating regasification terminals. The number of such terminals currently planned or in development is on the rise in South East Asian countries where the main purpose is to provide base load regasification where gas demand is constant and set to increase on a year-by-year basis. In contrast with the Atlantic floating regasification terminals, where only seasonal demands are required to be met, a base load terminal calls for a stricter requirement in terms of availability and offshore survivability, and hence the approach to marinisation for base load terminals is different to that for seasonal load terminals. Introduction One of the means of supplying natural gas to coastal markets is by importing liquefied natural gas (LNG) from distant, often overseas, suppliers and subsequently converting the LNG back to its gaseous state prior to injection into the gas supply grid. The process of vaporizing LNG to gas is referred to as regasification. Shore-based regasification terminals, which includes land-based and near shore jetty based terminals, have been in operation for several decades and the regasification technologies used on such terminals are mature and proven. However, shore-based regasification terminals generally require large land space. When combined with the lack of shore space, large capital cost and long duration needed to develop such terminals, as well as the emerging issues of NIMBY (Not In My Backyard) and public perception, the only alternative is to move to an offshore based terminal solution. One such offshore solution that addresses these issues is the floating regasification terminal, which combines LNG storage and the regasification equipment on a single moored floating vessel/hull. Such floating terminal is commonly known as Floating Storage Regasification Unit (FSRU).

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 score0.485
Threshold uncertainty score0.730

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 head0.068
GPT teacher head0.245
Teacher spread0.177 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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