Mass and heat transfer behaviour of catalytic and electrochemical stirred tank reactors employing metallic screens lining as a reaction surface
Bibliographic record
Abstract
Abstract Liquid‐solid mass transfer behaviour of a rectangular stirred tank reactor lined with single screens and stacks of closely packed screens was studied in relation to catalytic and electrochemical reactor design by the electrochemical technique. Variables studied were impeller rotation speed, mesh number, and wire diameter of the screen, as well as physical properties of the solution, number of screens per stack, and impeller geometry. The rate of mass transfer increased with increasing impeller rotation speed and decreased with increasing screen mesh number and number of screens per stack. Radial flow turbine impellers produced higher rates of mass transfer than axial flow impellers. The data were correlated by dimensionless mass transfer equations. A comparison between the volumetric mass transfer coefficient at a stirred single‐screen electrode and at a stirred flat plate electrode shows that the volumetric mass transfer coefficient at the single screen is higher than that at the flat plate by a factor ranging from 7.3–22.5, depending on the operating conditions. For screen stacks, the ratio between the stack volumetric mass transfer coefficient and the flat plate value ranges from 2–46 depending on the operating conditions. The importance of the present results in the design and operation of catalytic and electrochemical reactors used to conduct diffusion‐controlled reactions was highlighted. The possibility of using screens as turbulence promoters to enhance the rate of heat transfer between the reactor wall and the surrounding cooling jacket was noted.
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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".