<scp>RSM</scp> and <scp>ANN</scp> ‐based optimization of reactive extraction of propionic acid using tributyl phosphate with both conventional and natural diluents
Bibliographic record
Abstract
Abstract Propionic acid (PA) has a wide application in various food and chemical industries. In the present work, the reactive extraction of PA from aqueous solutions is done with eco‐friendly natural diluent alsi oil and harmful conventional diluents butanol and benzene, and the tri‐n‐butyl phosphate (TBP) as extractant. Design of experiments was done for the physical and reactive extraction using Box–Behnken design with response surface methodology (RSM). The effect of different factors, such as temperature, initial PA concentration, diluents, extractant, and aqueous‐to‐organic phase ratio, was analyzed. In the physical extraction, the extraction efficiency achieved 75.57% for butanol, 29.91% for benzene, and 47.57% for alsi oil. In the reactive extraction method, the maximum extraction efficiency of 96.89%, 92.54%, and 92.37% for TBP‐butanol, TBP‐benzene, and TBP‐alsi systems, respectively, was achieved. For reactive extraction, optimal conditions were 308 K, 0.1 mol/L initial concentration, a 1:1 volume ratio, and 25% extractant composition predicted by the RSM method and the artificial neural network (ANN) optimization method. ANN shows a better regression parameter ( R 2 = 0.962) than RSM. The higher percentage of extraction efficiency was achieved with conventional diluent butanol; however, the harmless natural diluent alsi oil shows better results, which makes it an alternative to hazardous conventional solvents in the industrial extraction process. These results can help design efficient extraction methods for recovering the PA from the aqueous wastewater stream. The extraction efficiency was achieved as TBP‐butanol > TBP‐benzene > TBP‐alsi oil.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".