Using dynamic tests to study the continuous mixing of xanthan gum solutions
Bibliographic record
Abstract
Abstract BACKGROUND: The current understanding and implementation of continuous mixing of non‐Newtonian fluids is insufficient to ensure good mixing in many cases. In this study, the dynamic response of the continuous mixing of xanthan gum solution, which is a pseudoplastic fluid with yield stress, was quantified using a dynamic model that incorporated non‐ideal flows within the mixing vessel. The model allowed for two parallel flow paths through the tank: (1) a channeling zone and (2) a mixing zone. RESULTS: Dynamic tests were made using the frequency‐modulated random binary input of a brine solution with the feed to determine the magnitude of non‐ideal flows. The extent of flow bypassing the mixing zone and the effective mixed volume were determined from dynamic tests and used as mixing quality criteria. We explored the effect of impeller speed, impeller type, feed flow rate through the mixing tank, fluid rheology, and feed and exit location on the degree of channeling and the fraction of fully mixed volume. Tests show that when the surface of the cavern created by impeller approaches the tank wall, the bottom of the tank, and the surface of the fluid within the mixing vessel, the percentage of non‐ideal flow approached zero. CONCLUSION: This study identifies important criteria for continuous mixing vessels that will improve mixing efficiency. By applying these findings, a reduction in the extent of non‐ideal flow can be achieved, which will improve the quality and control of the continuous mixing processes such as continuous high‐viscosity reactor, continuous fermenter, and continuous solid–liquid mixing. Copyright © 2008 Society of Chemical Industry
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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) | 0.000 | 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) | 0.001 | 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".