Recovery of volatile fatty acids by reactive extraction using tri‐<i>n</i>‐octylamine and tri‐butyl phosphate in different solvents: Equilibrium studies, pH and temperature effect, and optimization using multivariate taguchi approach
Bibliographic record
Abstract
Abstract Recovery of volatile fatty acids from fermentation broth has been investigated by adopting an intensified approach using extractants tri‐n‐octylamine and tri‐butyl phosphate dissolved in 1‐decanol and methyl isobutyl ketone. The effects on distribution coefficient (KD) and extraction efficiency (%E) were studied by varying the operating conditions like temperature (293.15–323.15) K, pH (2.5, 3.5, and 4.5), and compositions of extractant (10, 20, and 30 %). Taguchi (L36) orthogonal design with five factors, namely diluents, extractant type, composition, temperature, and pH, was employed for the multivariate optimization of reactive extraction of volatile fatty acids. In the Taguchi approach, a “larger is better” criterion was adopted to maximize and %E. The statistical analysis indicated that the degree of influence on by experimental variables follows the following trend: extractant type ( ) > pH ( ) > diluent type ( ) > temperature ( ) > extractant concentration ( ). The trend for %E observed is as follows: extractant type ( ) > pH ( ) > temperature ( ) > extractant concentration ( ) > diluent type ( ). The combination of optimum parameters were obtained as X1 = 1‐decanol, X2 = tri‐n‐octylamine, X3 = 20 %, X4 = 293.15 K, and X5 = 3.5. A confirmation run was conducted using these parameters and and %E values from this run were determined to be 8.65 and of 89.64 %, respectively, which were very close to the predicted values 10.36 and %E = 91.26 %.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".