Continuous cultivation of <scp><i>Debaryomyces hansenii</i></scp> (LAF‐3 10 U) on dodecane in synthetic desalter effluent at varying dilution rates on dodecane
Bibliographic record
Abstract
Abstract Desalter effluent (DE) is typically discharged into a petroleum wastewater treatment plant, but its high salt concentration deteriorates the biological treatment. This study used various dilution rates to investigate the treatment of a synthetic DE containing dodecane under saline conditions using a halotolerant yeast, Debaryomyces hansenii , to determine the optimum substrate concentration for use in continuous stirred‐tank reactors (CSTRs). A literature review indicated that this study was the first to examine the biological treatment of DE using D. hansenii in a CSTR system. At a low dodecane substrate concentration, DE did not inhibit D. hansenii growth, and the experimental data approached the Monod model, with μ max and K s selected as 0.08 h −1 and 1575 mg L −1 , respectively. The optimum removal of chemical oxygen demand (95.7% and 85%) was obtained at dilution rates of 0.007 and 0.026 d −1 . Using D. hansenii in a CSTR system appeared to be a sustainable approach for the biological treatment of DE. Scale‐up of these laboratory findings to the industrial scale is required to confirm that petroleum DE can be treated using equalization and filtration tanks as a continuous bioreactor. Adjusting the dilution rate can provide sufficient time for biodegradation and hydrocarbon removal from high salt DE by halotolerant yeasts like D. hansenii .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".