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Record W4403670929 · doi:10.1016/j.ces.2024.120823

Experimental measurements and thermodynamic modeling of dissociation pressure of CO2 hydrate in sulfate solutions

2024· article· en· W4403670929 on OpenAlexafffund
Ying Zhou, Zhuo Chen, Yu Wei, Nobuo Maeda, Huazhou Li

Bibliographic record

VenueChemical Engineering Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsClathrate hydrateHydrateDissociation (chemistry)ThermodynamicsSulfateChemistryPhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

• We report new data on dissociation pressure of CO 2 hydrate in sulfate solutions. • Sulfate solutions exhibit thermodynamic inhibition effects on CO 2 hydrate. • CuSO 4 solutions show slight inhibition effects on CO 2 hydrate. • The predicted dissociation pressure agrees reasonably well with the measured data. • The dissociation enthalpies depend on temperature and sulfate salt concentration. Carbon capture and storage (CCS) has been a popular strategy to mitigate climate change and has attracted significant attention from both industry and academia. CO 2 can be stored in the form of CO 2 hydrate in deeper locations in an ocean, which makes it a potential option for CO 2 sequestration. Dissolved salts in the ocean brine can significantly affect the dissociation pressure of CO 2 hydrate. Sulfate salts are one of the most common salt species in the oceanic brine. Although various experimental studies have been conducted to investigate the phase behavior of CO 2 hydrate in brine, few studies focus on the effect of sulfate salts on the dissociation pressure of CO 2 hydrate. In this study, we build an in-house experimental setup to investigate the effect of monovalent and divalent sulfate salts on the dissociation pressure of CO 2 hydrate. The dissociation pressure of CO 2 hydrate in Na 2 SO 4 , K 2 SO 4 , MgSO 4 , and CuSO 4 aqueous solutions is measured using the isochoric pressure search method at different concentrations over the temperature range of 274.36 – 282.15 K and the pressure range of 1.50 – 4.03 MPa. A hybrid methodology incorporating the Soave-Redlich-Kwong Equation of State (SRK EOS), the van der Waals-Platteeuw (vdW-P) model, and the Pitzer model is applied to predict the dissociation pressure of CO 2 in sulfate solutions. In addition, the dissociation enthalpies of CO 2 hydrate in these sulfate solutions are calculated using the Clausius-Clapeyron equation based on the measured dissociation points. The experimental results show that the dissociation pressure of CO 2 hydrate in Na 2 SO 4 , K 2 SO 4 , and MgSO 4 solutions is higher than that in pure water, and the dissociation pressure of CO 2 hydrate in sulfate solutions increases with an increasing salt concentration. Conversely, CuSO 4 barely affects the dissociation pressure of CO 2 hydrate, which is mainly attributed to the lower molar concentration of the ions compared with the other salt solutions and the ion specificity of Cu 2+ . The prediction results are in alignment with the experimental data measured in this study, which proves the feasibility of the thermodynamic model in predicting the dissociation pressure of CO 2 hydrate in sulfate solutions. Additionally, the calculated dissociation enthalpies of CO 2 hydrate show a dependence on both temperature and salt concentration. It is also revealed that Cu 2+ exhibits ion specificity in affecting the dissociation enthalpy of CO 2 hydrate, likely due to its more distinct ability to impact the cage occupancy of CO 2 hydrate than the other ions. These findings enhance our understanding of the impact of sulfate salts on the dissociation behavior of CO 2 hydrate and offer valuable insights for CO 2 sequestration.

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: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.241

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 head0.015
GPT teacher head0.224
Teacher spread0.208 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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