Bibliographic record
Abstract
Using the spray granulation technology,the asphalt particles could be obtained from the de-oiled asphalt which is the coupled post-extraction asphalt residue of the heavy oil deep separation and the possibility of producing asphalt particles provides a nice prospect for the utilization of de-oiled asphalt.In this paper,the influencing factors of asphalt particle production by spray granulation were studied with a continuous solvent deasphalting device;the feedstock used are Canada oil sand bitumen,Venezuela AR and VR,Middle East VR,respectively,and the other experimental operation conditions used respectively are as follows: n-butane,n-pentane,n-hexane and their mixture are used as solvents,temperatures at the bottom of the extraction tower are 130℃,146℃ and 150℃,the auxiliary solvent temperatures are 130℃ and 150℃,the operating pressure is 5 MPa,the mass ratio of solvent to feed is 4,the sizes of the nozzle(inside diameter) are 3 mm,4 mm and 8 mm and the nitrogen flow rates are 0.063 m·s-1 and 0.080 m·s-1.The patterns of the asphalt particles produced under different operating conditions were recorded by photographs and compared with each other.It was found that the influencing factors mainly focus on several aspects,such as the property of de-oiled asphalt,the temperature at the bottom of the extraction tower,the temperature of the auxiliary solvent,the inner diameter of the nozzle,the diffuse space for solvent and the nitrogen stripping.In order to produce the qualified asphalt particles conveniently,both the feedstock and solvent used should be selected to let the asphalt could have high hard component content.Besides,the suitable temperature at the bottom of extraction tower and of the auxiliary solvent,a small size nozzle used,an open space for solvent diffusing and reasonable flow rate of nitrogen stripping,all these are favourable for the process of asphalt spray granulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".