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Bioreaction coupled flow simulations: Impacts of methanogenesis on seasonal underground hydrogen storage

2023· article· en· W4388592852 on OpenAlexfundno aff
G. Wang, Gillian Elizabeth Pickup, K. S. Sorbie, Júlia R. de Rezende, F. Zarei, Eric Mackay

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2023
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilLeverhulme TrustEnergi SimulationComputer Modelling GroupHeriot-Watt University
KeywordsMethanogenesisMethanationHydrogenEnvironmental scienceHydrogen storageCarbon capture and storage (timeline)ChemistryMethanePetroleum engineeringGeologyClimate changeOceanography

Abstract

fetched live from OpenAlex

Assessing microbial risks is key to feasible hydrogen storage in geological formations. This work quantitatively analyses the impacts of bio-methanation on hydrogen storage performance. Fine-scale flow simulations, coupled with the bio-methanation reaction, are presented to analyse its impact on the storage performance. Based on the reported rates in literature, methanogenesis may slightly degrade the recovery performance of hydrogen but is considered minor compared with the issue of gas mixing. The impacts of methanogenesis on a time scale of months (330 days) becomes observable in the system configured here, when the methanation rate is above 1746 nano molality per hour. The assumed methanation rate is two times greater than the rate reported from the Olla filed. Validated scaling theory generalises findings for gravity-dominated scenarios. But viscous-dominated flows see complications from property variations due to pressure changes at high rates. This study provides definitions of “target properties” (e.g., acceptable methanogenesis rates) for screening hydrogen storage projects.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.253
Teacher spread0.238 · 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

Citations34
Published2023
Admission routes1
Has abstractyes

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