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Record W2091736630 · doi:10.1021/ie048998k

Role of Acidified Sodium Silicate in Low Temperature Bitumen Extraction from Poor-Processing Oil Sand Ores

2005· article· en· W2091736630 on OpenAlexafffund
H. Li, Zhenlu Zhou, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphaltOil sandsSodium silicateExtraction (chemistry)ChemistryDispersantIlliteSlurryChemical engineeringSodiumMineralogyMetallurgyMaterials scienceClay mineralsChromatographyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Process aids are generally required to improve bitumen recovery from poor-processing oil sand ores containing a relatively high amount of divalent metal ions and fine solids/clays. In this paper, the role of acidified sodium silicates as a dispersant/depressant of clay fines in bitumen extraction was evaluated using a laboratory hydrotransport extraction system (LHES) at low temperature (35 °C). Bitumen recovery experiments showed that adding acidified silicates during the bitumen extraction process resulted in a higher degree of bitumen liberation from sand grains, a faster bitumen flotation rate, and a better bitumen froth quality than adding caustic. Solution chemistry analysis demonstrated that acidified sodium silicate is a better process aid than caustic because it has three functions: to precipitate calcium and magnesium in the process water, which minimizes the synergistic effect of divalent cations in inducing a clay coating on the bitumen surface and clay gelation; to maintain an adequate pulp slurry pH for better bitumen−air bubble attachment; and to disperse/depress clay fines from flotation by its specific species.

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 categoriesInsufficient payload (model declined to judge)
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 score0.015
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.313
Teacher spread0.280 · 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.

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

Citations36
Published2005
Admission routes2
Has abstractyes

Explore more

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