MétaCan
Menu
Back to cohort
Record W3116813531 · doi:10.11575/prism/38486

Water content of liquid acid gas and liquid propane in the presence of a hydrate phase

2020· dissertation· en· W3116813531 on OpenAlexfundno aff
Kayode I. Adeniyi

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPropaneHydrateClathrate hydrateChemistryPhase (matter)Gas phaseLiquid phaseChromatographyChemical engineeringOrganic chemistryEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Natural gas coexists with water in subsurface reservoirs. Other impurities such as hydrogen sulfide (H2S) and carbon dioxide (CO2) can also be present depending on location and source. Many issues are associated with the presence of water and the acid gas (H2S and CO2) impurities during production, processing and transportation of natural gas such as solid hydrate blockage, corrosion and safety concerns. Before sale to consumers, water and the acid gas impurities are removed or reduced to meet sales and pipeline specifications. One of the viable strategies for managing the removed acid gas is injection (AGI) into underground formations either for sequestration, pressure maintenance or enhanced oil recovery. After acid gas removal, in some cases, natural gas liquid (NGL) are separated from the treated natural gas streams for use as a fuel or chemical feedstock. NGL are separated from the methane (CH4) in a cryogenic separation process, where the presence of water is highly undesirable because it can cause the formation of solid clathrate hydrates. Propane (C3H8) is a principal component of NGL and a sII hydrate former; hence, conditions at which its hydrate will form in the presence of saturated and unsaturated water are important to avoid their formation or determine how much dehydration is required. Because of the toxicity of H2S (100 ppm is the immediate dangerous to life and health concentration), there are limited dissociation data for its hydrate in the presence of water reported in the literature. Also, prior to this work, there were no water content data in equilibrium with only hydrate reported in the literature for H2S. On the other hand, hydrate formation/dissociation conditions for pure CO2 are well studied; however, analysis of the literature water content data at hydrate forming regions shows some variation regarding the pressure dependence of the measurements. These data are necessary to accurately calculate and prevent hydrate formation conditions in sour natural gas production, as well as to define the dehydration requirements for acid gas during transportation to injection facilities. In this work, the dissociation conditions for pure CO2 and pure H2S hydrates in the presence of water rich phase was measured using the phase boundary dissociation method. Also, the water content of pure CO2, pure H2S and pure C3H8 in equilibrium with their respective hydrate were measured using a tunable diode laser spectroscopy technique. These results were modelled using the reference quality Helmholtz energy equations of state for the fluid phases, and the van der Waal and Platteeuw model for the hydrate phases. The calculated results were compared to few available literature, where a good agreement was mostly observed. A thermodynamic model capable of calculating the water content and three phase loci independently using the same optimized parameters, was successfully developed for CO2. However, a single equilibrium model was not successfully found for H2S and C3H8 fluids, hence two different models were recommended for both the water content and three phase loci calculation. In conclusion, the pressure dependence of water content of these gases in equilibrium with their respective hydrates are very weak, but the water content increases as the temperature increases.

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 categoriesInsufficient payload (model declined to judge)
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 score0.023
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.289
Teacher spread0.249 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOpen MINDSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207