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Record W2152939055 · doi:10.1002/cjce.20671

Feasibility of recycling KOH in chemical activation of oil‐sands petroleum coke

2011· article· en· W2152939055 on OpenAlexafffundvenueabout
Mingjiang Yuan, Yesul Kim, Charles Q. Jia

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPotassium hydroxidePetroleum cokeChemistryChemical engineeringCarbon fibersPotassiumCokeHydrogenMetalActivated carbonInorganic chemistryMaterials scienceOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Abstract Although potassium hydroxide (KOH) is known to be effective in generating highly porous activated carbons, the mechanism of KOH activation has not been well elucidated. To develop porosity in carbon, a high KOH/carbon mass ratio must be maintained. Consequently, KOH, as the activating agent, represents a major part of the cost of the activation process. Focusing on the mechanism, particularly the activation products, the present work attempted to establish the technical feasibility of recycling KOH. Experiments revealed that the major products of KOH activation at 600–900°C are metallic K, K 2 CO 3 , CO and H 2 , which is supported by thermodynamic analysis. The overall reaction may be written as 6KOH + 4C = K 2 CO 3 + 4K + 3H 2 + 3CO. At temperatures over 900°C, K 2 CO 3 becomes unstable and participates in activation reactions with carbon; a more suitable overall reaction would be KOH + C = CO + K + 0.5H 2 . As potassium ion is reduced to metallic K which is readily converted into KOH and hydrogen gas upon reacting with water, KOH recycling is feasible. The reuse of KOH in chemical activation could substantially reduce the cost of activation process. © 2011 Canadian Society for Chemical Engineering

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)0.001
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 score0.005
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.026
GPT teacher head0.208
Teacher spread0.181 · 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 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

Citations39
Published2011
Admission routes4
Has abstractyes

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