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Record W4327980547 · doi:10.1007/s42773-023-00209-x

Adsorption of methane on biochar for emission reduction in oil and gas fields

2023· article· en· W4327980547 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiochar · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of OttawaFPInnovationsNatural Resources Canada
FundersNatural Resources Canada
KeywordsBiocharMethaneAdsorptionFossil fuelGreenhouse gasWork (physics)Waste managementEnhanced coal bed methane recoveryChemical engineeringEnvironmental scienceChemistryMaterials sciencePetroleum engineeringPulp and paper industryCoalPyrolysisOrganic chemistryThermodynamicsGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract To contribute to the reduction of methane emissions, using low-cost biochar as adsorbents for capturing and storing methane in oil and gas fields is investigated. This work presents results of methane adsorption on four biochars made from forestry wastes in comparison with the results of three commercial activated carbons. Although the adsorption capacity of the biochars is lower by over 50% than that of the activated carbons, thelow-cost and potential environmental benefits provide the incentive to the investigation. Moreover, it is shown that biochar can store more methane than vessels of compressed gas up to the pressure of 75 bar, suggesting the possibility of avoiding high-pressure gas compression and heavy vessels for cost savings in oil and gas fields. The thermodynamic and kinetic behaviors of the adsorption are studied and implications for the targeted application are discussed. Graphical Abstract

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.

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 score0.117
Threshold uncertainty score0.278

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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