Desulphurization of gas oil in a packed bed extractor: Optimization of operating parameters for simultaneous maximization of efficiency and yield by desirability approach
Bibliographic record
Abstract
Abstract A full factorial central composite design (CCD) method was used to design the experiments for extractive desulphurization of straight run gas oil (SRGO) containing 0.013 g/g (1.3 wt%) sulphur in a packed bed extractor using N ‐ N ‐dimethyl formamide (DMF) as solvent. The operational parameters, namely water concentration (W C ) in main solvent, solvent‐to‐feed ratio (S/F), and extraction temperature (T E ) which affect the sulphur removal and yield were used as input variables in design of experiments. Considering the trade‐off between sulphur removal and yield, multi‐response optimization with a desirability function approach has been used to estimate the optimized value of these operating parameters so as to maximize sulphur removal and yield of extracted straight run gas oil (ESRGO). Optimum values of selected variables were: water content in solvent = 2.91, solvent‐to‐feed ratio = 1.70, and extraction temperature = 46.4 °C. At the maximum desirability value, ESRGO yield and percent sulphur removal were 81.67 and 60.53 %, respectively. Since importance of sulphur removal and yield would depend on the secondary process to be selected for reducing the sulphur to 50–10 ppm, an analysis of goal importance effect on the optimized value of operational parameters for maximum desirability was also presented and discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".