Exergy Analysis of Single- and Two-Stage Crude Oil Distillation Units
Bibliographic record
Abstract
In a refinery distillation plant, there are many components of interest to be analyzed thermodynamically, e.g., the crude oil heating furnace, the distillation column and a network of heat exchangers. Previous studies showed that the highest exergy losses occur when there is a heat transfer process especially in the crude oil heating furnace where high quality fuel is used to heat the crude oil, which is a low quality duty, beside the high temperature difference. Therefore, it is proposed in this work to perform distillation in two stages rather than one to reduce heat duty of the heating furnace and thus reducing irreversible losses. In this paper, energy and exergy analyses of a traditional one-stage crude oil distillation unit and a newly proposed two-stage crude oil distillation unit are conducted to study energy and exergy efficiencies of these units and determine the exergy losses. The results are compared for both one- and two-stage distillation units. In this regard, a commercial software package, SimSci/PRO II program is used to carry out both energy and exergy calculations. It is found that the overall exergy efficiencies for single- and two-stage distillation units are 14.0% and 31.5%, respectively. The proposed two-stage distillation unit shows 43.8% decrease in the overall exergy losses and 125% increase in the overall exergy efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".