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Record W2780789643 · doi:10.5539/mas.v12n1p128

CO2 and Methane Separation Using Finger-Type Slug Catcher at Seabed

2017· article· en· W2780789643 on OpenAlexvenueno aff
Leksono Mucharam, Silvya Dewi Rahmawati, Ery Budiono

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersInstitut Teknologi Bandung
KeywordsInletMethaneRange (aeronautics)SeabedPhase (matter)Materials scienceSeparation (statistics)MechanicsEnvironmental scienceDegree RankineSlug flowPetroleum engineeringMarine engineeringGeologyTwo-phase flowMechanical engineeringThermodynamicsChemistryEngineeringFlow (mathematics)Computer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Gas production with a very high CO2 content requires special treatment to separate the methane from CO2. The separation also requires high capital expenditure (CAPEX) and operational expenditure (OPEX).The challenge is higher, when the gas is being produced on offshore, and only narrow space that available on the platform for separation process. One proposed method to do the separation is by shifting the CO2 phase in the phase diagram from the gas phase to the liquid phase. This shiftting requires certain pressures and temperatures that meet the temperature and pressure boundary to become liquid.This study performs a simulation to determine the pressure and temperature constraints required to convert CO2 from the gas phase into the liquid phase. This study uses a finger-type slug catcher mounted on the seabed. The finger-type slug catcher consists of parallel pipes of a certain diameter and length. Furthermore, the simulation is done by variations of several variables, such as: inlet pressure, ambient temperature, inner pipe diameter, and number of branches. The aim of the research is to design a slug catcher model so that the separation of methane and CO2 gas can occur optimally. The slug catcher design resulting from this study includes the value of the inlet pressure, diameter, and minimumlength of the pipe where CO2 begin to form liquid.The simulation was done by 320 kinds of combination for the range of inlet pressure value from 800 to 1500 psia, the range of pipe inner diameter is 40 - 50 inch, the range of ambient temperature is 50 - 80oF, and the range of 1 to 5 number of branches. Based on the simulation, when the inlet rate of 200 MMSCFD, the inlet temperature of 100oF and the overall heat transfer coefficient of 200 BTU/ hr / ft2 / oF, it is obtained the shortest length of slug catcher is 72.18 ft, 40-inch inner diameter, and the inlet pressure requirement is 1000 psia. Furthermore, for design purposes, the slug catcher length is made 217 ft to ensure that the liquid CO2 formed at 72.18 ft from the inlet and the liquid can accumulate and keep flowing towards the end of the pipeline for further utilization.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.034
GPT teacher head0.296
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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