Liquid distribution of MEA in random and structured packing in a square column
Bibliographic record
Abstract
Liquid flow distribution in a square column was analyzed to evaluate the contact between liquid and gas phases in an absorption column, and to compare the results from a conventional cylindrical column. The square column was 12 inches wide by 12 inches deep by 40 inches tall. It was constructed with glass walls so that the flow pattern could be observed. The liquid distributor was designed with a pattern that would deliver the flow evenly in a set pattern over the packing with a drip per area ratio of 321/ft 2 (2.23/inch 2 ). Experiments were conducted using both random and structured packing. A total packing volume of 3 ft 3 was used in each study. One-inch Pall Rings were used for the random packing study. For the structured packing tests, we designed and developed an innovative packing for the ITC pilot plant. Three packing bricks were used in the column. The testing of the structured packing was done using two different packed brick orientations; one with all of the bricks in the same flow direction, the other with the middle brick rotated 90 ∘ from the orientation of the top and bottom bricks. A monoethanolamine (MEA) solution concentration of 3.0 molar was used in the experiments. The liquid distribution tests were done at one flow rates. Since the viscosity of the MEA solution was affected by the carbon dioxide (CO 2 ) solution loading, experiments were also conducted to evaluate the effects of CO 2 loading on the liquid hold-up. Our goal is to develop a cost effective structure packing that performs at a level that is comparable to commercially available packing, but with lower manufacturing and installation cost.
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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 | 0.000 | 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".