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Record W73299593 · doi:10.1063/1.2721261

Upgrading of Bitumen in Super Critical Water — Activation of Water

2007· article· en· W73299593 on OpenAlexaboutno aff
Daisuke Miyamoto, A. Kishita, Fangming Jin, T. Kazuyuki, H. Enomoto

Bibliographic record

VenueAIP conference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSupercritical fluidAsphaltSupercritical water oxidationOil sandsAlkali metalChemical engineeringMaterials scienceWaste managementPetroleum engineeringSulfurBatch reactorChemistryMetallurgyCatalysisOrganic chemistryGeologyComposite material

Abstract

fetched live from OpenAlex

Recently, oil sand in Canada has been gathering attention because of its huge storage, and the lack of fossil fuels. However, the bitumen from the oil sand has the problems for commercial use due to its high viscosity, high sulfur and high heavy metal content. Our research group has been carrying heavy oil/bitumen upgrading by hydrothermal visbreaking in supercritical water with the addition of alkali. Results showed that by supercritical water treatment with alkali was very effective for upgrading heavy oil/bitumen. However, water activation for upgrading bitumen remains poorly understood. An understanding of water activation is needed for improving upgrading bitumen in supercritical water reaction. In this study, therefore, the effect of water on upgrading bitumen in supercritical water reaction was investigated by varying reaction pressure and temperature. Experiments were performed in both a batch reactor and a continuous‐flow reactor. In batch experiment, experiments were performed at temperature of 380°C to 460°C, the reaction pressure of 24 MPa to 40 MPa, and the reaction time of 30 min. Results showed that pressure and temperature, particularly pressure, have great effect on upgrading bitumen in supercritical water reaction This suggests that that the desired product could be obtained by controlling the reaction pressure and temperature.

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 score0.013
Threshold uncertainty score0.420

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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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