Using electrical resistance tomography to characterize and optimize the mixing of micron sized polymeric particles in a slurry reactor
Bibliographic record
Abstract
A solid-liquid mixing system has a significant role in the suspension polymerization, crystallization, adsorption, and solid-catalyzed reactions. In this study, Electrical Resistance Tomography (ERT) was employed to investigate the effect of the particle size, the design parameters such as impeller type, impeller clearance and impeller diameter as well as operating conditions such as impeller speed, impeller pumping mode, and solids concentration on the mixing of micron sized latex particles in a slurry reactor. The ERT data were used to calculate the concentration profile and the degree of homogeneity in three dimensions, as a function of design parameters and operating within the reactor. In this work, tap water and latex particles (5.2 µm, 8.5 µm, 9.1 µm) were used as liquid and solid phase, respectively. Six axial impellers were utilized (A310, A100, A200, A320, A315, 3AM) with impeller speed (N) varying from 252 rpm to 400 rpm. Impeller diameter to tank diameter ratios (D/T) were in the range of 0.29 to 0.47 while, the impeller clearance (C/T) was changed in the range of T/3.8 to T/2.5. Impeller pumping was tested in both downward and upward directions. The concentration of latex particles was ranged between 15 wt% and 30 wt%. This study shows that the level of homogeneity in a solid-liquid mixing system improved with the increase in impeller speed. However, after achieving the maximum level of homogeneity, any further rise in the impeller speed had a detrimental effect on the level of homogeneity. A310 impeller, wtih D/T ratio of 0.31, demonstrated the highest level of homogeneity while the upward pumping direction was found to be more efficient than the downward one. In addition, a clearance of T/3 proved to create the highest level of homogeneity. Also, the results showed that a rise in the size and concentration of particles decreases the level of homogeneity. Thus, 5.2 µm latex particles with the concentration of 15 wt% demonstrated the highest level of homogeneity. Applying the findings of this study will lead to improved equipment design, chemical cost reduction, increased production rate, improved quality of products, and more efficient use of power in slurry reactors.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".