Critical review of different aspects of liquid-solid mixing operations
Bibliographic record
Abstract
Abstract Mechanically stirred slurry tanks are utilized in several industries to perform various unit operations such as crystallization, adsorption, ion-exchange, suspensions polymerization, dispersion of solid particles, leaching and dissolution, and activated sludge processes. The major goal of this review paper is to critically and thoroughly analyse the different aspects of previous research works reported in the literature in the field of liquid-solid mixing. This paper sheds light on the advantages and limitations of various particle concentration measurement methods employed to assess the suspension quality and the extent of solid suspensions in slurry reactors. Attempts are being made to identify and compare various mathematical models and methods to quantify particle dispersion and distribution in slurry reactors. It has been shown that various factors such as geometric configurations, agitation conditions, and physical characteristics of liquid and solid have pronounced influence on local suspension quality and power consumption. Computational fluid dynamics (CFD) modeling can be extremely useful in assessing the suspension of solid particles in slurry tanks. A critical review of different scale-up procedures employed for solid suspension and distribution in liquid-solid systems is presented as well. The findings of this review paper can be useful for future research works in liquid-solid mixing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".