Response surface methodology ( <scp>RMS</scp> ) modelling to improve <i>n</i> ‐dodecane degradation using <i>Debaryomyces hansenii</i> <scp>LAF</scp> ‐3 in a simulated desalter effluent
Bibliographic record
Abstract
Abstract The effluent from a petroleum desalting unit contains salts, emulsifiers, hydrocarbons (mainly n ‐dodecane), and other contaminants. Conventional wastewater treatment cannot be used to treat this salt‐containing effluent; thus, alternative approaches must be explored. In this study, a halo‐tolerant yeast, Debaryomyces hansenii , was investigated for the removal of varying concentrations of n ‐dodecane in a simulated desalter effluent (SDE). Then, the removal of n ‐dodecane was optimized using response surface methodology (RSM). The effects of pH, salt, temperature, and n ‐dodecane concentration were evaluated, and a mathematical model was developed and verified. The results showed that complete removal of n ‐dodecane was achieved at 20°C and a salt concentration of 1–5 g L −1 . The main factors in COD removal are temperature, n ‐dodecane concentration, pH, and the interaction between n ‐dodecane and temperature. Salt concentration does not affect COD removal or the growth rate of D. hansenii in a SDE. Applying RSM suggested that interactional effects among the operational variables include temperature, n ‐dodecane concentration, and pH on the yeast's removal rate. Overall, it can be concluded that the use of D. hansenii could be a viable solution at a wide range of salt concentrations (1–5 g L −1 ) for desalter wastewater treatment in petroleum refining.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".