Development of a model to determine mass transfer coefficient and oxygen solubility in bioreactors
Bibliographic record
Abstract
The objective of this paper is to present an experimentally validated mechanistic model to predict the oxygen transfer rate coefficient (Kla) in aeration tanks for different water temperatures. Using experimental data created by Hunter and Vogelaar, the formula precisely reproduces experimental results for the standardized Kla at 20 °C, comparatively better than the current model used by ASCE 2–06 based on the equation Kla 20 = Kla. ( θ ) (20−T) where T is in °C. Currently, reported values for θ range from 1.008 to 1.047. Because it is a geometric function, large error can result if an incorrect value of θ is used. Establishment of such value for an aeration system can only be made by means of series of full scale testing over a range of temperatures required. The new model predicts oxygen transfer coefficients to within 1% error compared to observed measurements. This is a breakthrough since the correct prediction of the volumetric mass transfer coefficient (Kla) is a crucial step in the design, operation and scale up of bioreactors including wastewater treatment plant aeration tanks, and the equation developed allows doing so without resorting to multiple full scale testing for each individual tank under the same testing condition for different temperatures. The effect of temperature on the transfer rate coefficient Kla is explored in this paper, and it is recommended to replace the current model by this new model given by: K l a 20 = K l a ( E ρ σ ) 20 ( E ρ σ ) T ( T 20 T ) 5 where T is in degree Kelvin, and the subscripts refer to degree Celsius; E, ρ, σ are properties of water. Furthermore, using data from published data on oxygen solubility in water, it was found that solubility bears a linear and inverse relationship with the mass transfer coefficient.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".