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Record W2213546752 · doi:10.3390/ma9010020

Activation of Aspen Wood with Carbon Dioxide and Phosphoric Acid for Removal of Total Organic Carbon from Oil Sands Produced Water: Increasing the Yield with Bio-Oil Recycling

2016· article· en· W2213546752 on OpenAlexaff
Andrei Veksha, Tazul I. Bhuiyan, Josephine M. Hill

Bibliographic record

VenueMaterials · 2016
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsActivated carbonPyrolysisPhosphoric acidAdsorptionCarbon fibersChemistryCarbon dioxideYield (engineering)Pulp and paper industryWaste managementMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Several samples of activated carbon were prepared by physical (CO₂) and chemical (H₃PO₄) activation of aspen wood and tested for the adsorption of organic compounds from water generated during the recovery of bitumen using steam assisted gravity drainage. Total organic carbon removal by the carbon samples increased proportionally with total pore volume as determined from N₂ adsorption isotherms at -196 °C. The activated carbon produced by CO₂ activation had similar removal levels for total organic carbon from the water (up to 70%) to those samples activated with H₃PO₄, but lower yields, due to losses during pyrolysis and activation. A method to increase the yield when using CO₂ activation was proposed and consisted of recycling bio-oil produced from previous runs to the aspen wood feed, followed by either KOH addition (0.48%) or air pretreatment (220 °C for 3 h) before pyrolysis and activation. By recycling the bio-oil, the yield of CO₂ activated carbon (after air pretreatment of the mixture) was increased by a factor of 1.3. Due to the higher carbon yield, the corresponding total organic carbon removal, per mass of wood feed, increased by a factor of 1.2 thus improving the overall process efficiency.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.299

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 head0.007
GPT teacher head0.175
Teacher spread0.168 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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