Drop sizes during turbulent mixing of toluene–heavy oil fractions in water
Bibliographic record
Abstract
Abstract The properties of heavy oil emulsions produced in heavy oil extractions processes often fluctuate, thus making treatments difficult. We investigated whether oil‐phase ratios and flow conditions might influence these emulsions. We studied turbulent mixing effects on droplet sizes in real time, using a fully baffled stirred tank with a Rushton turbine in bench‐scale batch experiments. Breakup/coalescence of various volume fractions of toluene diluted heavy oil in model process water was measured at four mixing speeds. The range of oil volume fractions was 0.01 to 0.3, where 0.01 is the Kolmogorov limit and 0.3 the upper limit for oil‐in‐water (O/W) emulsions. Results showed that size distributions depended on mixing time, rpm, and oil fractions. Breakage dominated at low oil fraction 0.01 and high mixing intensities produced bimodal distributions. The persistence of finer droplets was attributed to reduced coalescence. Steady state was not reached. The middle range oil fractions (0.05, 0.1) approached steady state more quickly and followed a first‐order breakage model at 800 rpm. The size distributions narrowed before the end of the mixing time. The highest oil fractions and lowest mixing speeds produced the largest droplet sizes. Plots of d 32 vs. rpm for 75 min mixing showed that as the volume fraction of oil phase increased the shapes of the curves changed from concave to linear to convex. The d 32 vs. energy dissipation curves suggested that turbulent dampening reduced breakage. However, drop coalescence from erosive collisions as well as droplet surface elasticity were factors affecting droplet sizes. © 2006 American Institute of Chemical Engineers AIChE J, 2006
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".