Comparison of Continuous Blend Time and Residence Time Distribution Models for a Stirred Tank
Bibliographic record
Abstract
In continuous operation, mixing in a stirred tank is often characterized by the residence time distribution (RTD) curves and the mean residence time ( V / Q ). The RTD is a measure of the history of the fluid element flowing through the reactor rather than a measure of the local mixing conditions inside the vessel. In this study, additional information about local mixing is obtained by taking measurements inside the vessel. The variance of concentration fluctuations from three probes (two located inside the tank and one at the outlet) is used to determine the continuous blend time (θ cnts ). At the limiting condition of a slow feed rate relative to the batch blend time, the CSTR is ideal, but at high flow rates the mixing inside the vessel deviates by up to 50% from the ideal case. Three design guidelines are recommended for designs where ideal mixing conditions are required. First, a line from the inlet to the outlet should pass through the impeller. Second, the feed velocity should decay to the mean impeller suction velocity by the time the feed reaches the impeller for the case of surface feed above a downpumping impeller. Third, the ratio of the mean residence time ( V / Q ) to the batch blend time (θ b ) should be at least 10. Additional guidelines will be needed for tank configurations where the feed(s) and/or outlet(s) are located on the side of the vessel.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".