Effect of Geometrical dimensions and wastewater Temperature on the performance of an Induced Air Flotation unit for the treatment of industrial waste water
Bibliographic record
Abstract
Induced Air Flotation (IAF) systems had found great deal of interest for their high performance in oil/water separation for the treatment of municipal and industrial wastewater that can be achieved with simple and cheap operation compared with other systems such as the diffused air flotation. This paper is dedicated to the study of effects of geometrical dimensions, and water temperature on the separation efficiency of IAF process that handles effluent of Dairy Industry. Operation of IAF was based on the best factors gained by Al-Maliky, 2009 for the treatment of effluent of Dairy Industry. Results have shown that, ratios higher than 1:1 (width: height) of flotation tank tend to increase the separation efficiency under the same detention time; 1.25:1, 1.5:1, 1.75:1 and 2:1 had made an addition of 4.5, 5.2, 8.3 and 10.12% to the efficiency gain made by 1:1. Far from 2:1 was appeared to have a reverse effect in reducing the separation efficiency. The raise in wastewater temperature had shown a significant role in enhancing the water/oil separation efficiency, until it reaches about 80-84 ?C, beyond which that efficiency tends to decrease as oil flocs tend to scrap and layer disturbances take place. An enhancement of 10-17% occurs when operating the system with wastewater temperature of 80-84 C, and air flow rates of 3-5 l/min, respectively compared with that of 22 ?C and the same air flow rates. This paper was accomplished and edited with the use of logistic and technical resources of the Scholars Rescue Fund SRF and College of Engineering/ Ohio University.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".