Asphaltene Flocculation Inhibition with Ultrasonic Wave Radiation: A Detailed Experimental Study of the Governing Mechanisms
Bibliographic record
Abstract
The concept of ultrasonic wave assisted asphaltene flocculation/deposition inhibition was earlier introduced by Najafi et al., (2011). Current study is based on a series of experimental analyses, rheological changes, flocculation behavior, and total asphaltene content of two types of crude oils.The role of ultrasonic wave radiation on these parameters are investigated in order to elaborate the changes in the kinetics of asphaltene flocculation which leads to inhibition of asphaltene flocs formation. Based on the results obtained from the experiments, one can conclude that ultrasonic wave radiation can be most effective if the wave is radiated up to an optimum time. Around this time the oil has its local minimum value of kinematic viscosity, the least value of asphaltene content, and least potential for formation of macro-structured flocsdue to reduction of aromatic to saturate ratio. More detailed studiesrevealed that during radiation timetwo main mechanisms are active in asphaltenic crude oil: asphaltene particles disintegration and formation of asphaltene particles. Based on asphaltene content analysis, the optimum radiation time can be defined as the time at which the two mechanisms have an equal rate. The optimum radiation times are observed to be in the same time range in different tests. According to the results obtained, ultrasonic wave technology can be a potential method of flocculation inhibition and can have extensive industrial application. Key words: Ultrasonic; Asphaltene; Flocculation inhibition
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".