Improving the Efficiency and Capacity of Methanol−Water Distillation Trays
Bibliographic record
Abstract
The efficiency and capacity of valve trays in methanol−water distillation were studied in a 0.3-m-diameter column, covering a wide range of mixture compositions. The test results exhibit three unusual features. First, the efficiency was found to be substantially lower at both very high and very low methanol concentrations. Results indicated that the unique feature is closely related to the surface tension gradient, or the so-called “Marangoni effect”. Second, the capacity of the valve tray was affected by impurities present in the methanol−water mixture. Methanol−water mixtures from an industrial methanol purification column had impurities foamed present and resulted in lower capacities. This too is attributable to the Marangoni effect. Third, the capacities of the valve trays were strongly dependent on the methanol concentration. At increased water concentrations, the flooding-point F factors increased from 4.0 to 6.5 (kg/m) 0.5 /s. The use of structured packing as a de-entrainment device (DED) between the trays increased the capacity substantially. The DEDs also enhanced the efficiencies at high F factors. Additional tests in a 0.3-m air−water column confirmed the effectiveness of the DEDs. It was also found that the DEDs reduced the pressure drop of the valve trays as a result of entrainment suppression.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".