Biodesulfurization of Sour Crude Oil
Bibliographic record
Abstract
Crude oil is one of the most important types of fossil fuel in the world. It is an economically important commodity that is massively used in many industrial activities. The poor quality of crude oil is related to high sulfur content, which translates to lower profit margins and negatively impacts air quality standards. Polyaromatic sulfur heterocycles (PASHs) that exist in crude oil requires an efficient reduction method to achieve significant desulfurization levels. Recently, biodesulfurization (BDS) is gaining greater attention attributed to its environmentally benign bioprocess; possible benefits of BDS include lower capital and processing costs. Studies have reported that BDS is urgently needed for desulfurization of recalcitrant organic sulfur relative to traditional approach, hydrodesulfurization (HDS). The establishment of commercial scale biorefining technology relies on major advancement with respect to less expensive and sufficient production of highly active and stable biocatalysts that can be adapted to intense conditions encountered in petroleum refineries. In this paper, a review on BDS processes for removing recalcitrant thoiphenic components from sour crude oil is conducted, covering the aim of most studies concerning desulfurizing bacteria, which enables a deep desulfurization of organosulfur compounds by 4S pathway, maintaining the caloric value of fuel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".