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Record W2315704612 · doi:10.1021/ef402359b

Direct Coal Liquefaction: Low Temperature Dissolution Process

2014· article· en· W2315704612 on OpenAlexafffund
Fatemehalsadat Haghighat, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsUniversity of Alberta
FundersCanadian Centre for Clean Coal/Carbon and Mineral Processing Technologies
KeywordsCoalDissolutionLiquefactionExtraction (chemistry)Yield (engineering)SolventCoal liquefactionVolume (thermodynamics)ChemistryChemical engineeringMineralogyChromatographyPulp and paper industryMaterials scienceOrganic chemistryThermodynamicsComposite material

Abstract

fetched live from OpenAlex

The front-end design of a direct coal liquefaction process for the conversion of lignite into coal liquids by solvent extraction was investigated. The experimental work focused on physical coal dissolution in the temperature range 25–150 °C. It was found that the kinetics of physical coal dissolution was rapid and essentially complete within 2 min at 25 °C. There was a limiting extract yield, which increased with increasing temperature. Within the pore diameter range 0.1–10.7 μm, the volume of only pores with diameters <5 μm increased measurably on solvent extraction, while the shape of the pore size distribution remained the same. Additional pore volume created during extraction exceeded that of the liquid extract. Extraction took place from the bulk of the coal. Packed bed extraction was more efficient than batch extraction at otherwise similar conditions; an explanation was proposed. Even at the least severe conditions, 25 °C for 2 min, mass transport was not limiting and the solvent-to-coal ratio did not meaningfully affect the extract yield. These observations were employed to propose potential improvements to the front-end design for direct coal liquefaction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.194
Teacher spread0.188 · 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 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

Citations34
Published2014
Admission routes2
Has abstractyes

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