A novel method of simulating oxygen mass transfer in two‐phase partitioning bioreactors
Bibliographic record
Abstract
An empirical correlation, based on conventional forms, has been developed to represent the oxygen mass transfer coefficient as a function of operating conditions and organic fraction in two-phase, aqueous-organic dispersions. Such dispersions are characteristic of two-phase partitioning bioreactors, which have found increasing application for the biodegradation of toxic substrates. In this work, a critical distinction is made between the oxygen mass transfer coefficient, k(L)a, and the oxygen mass transfer rate. With an increasing organic fraction, the mass transfer coefficient decreases, whereas the oxygen transfer rate is predicted to increase to an optimal value. Use of the correlation assumes that the two-phase dispersion behaves as a single homogeneous phase with physical properties equivalent to the weighted volume-averaged values of the phases. The addition of a second, immiscible liquid phase with a high solubility of oxygen to an aqueous medium increases the oxygen solubility of the system. It is the increase in oxygen solubility that provides the potential for oxygen mass transfer rate enhancement. For the case studied in which n-hexadecane is selected as the second liquid phase, additions of up to 33% organic volume lead to significant increases in oxygen mass transfer rate, with an optimal increase of 58.5% predicted using a 27% organic phase volume. For this system, the predicted oxygen mass transfer enhancements due to organic-phase addition are found to be insensitive to the other operating variables, suggesting that organic-phase addition is always a viable option for oxygen mass transfer rate enhancement.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".