New Equations Predicting the Best Performance of Electrostatic Desalter
Bibliographic record
Abstract
Production of wet crude due to the raise in oil-water contact in many Iranian oil fields had been a growing field problem. Application of the right technology and installation of proper desalting facilities were required to solve this problem. Therefore, it was decided to install electrostatic desalting plants progressively in Iranian oil fields. By end of 2004, more than 20 plants with a total capacity of 1.3 MMSTB/day of treated crude had been installed. It is expected that production of wet crude will rise to 2.3 MMSTB/day in Iran by the year 2007. The performance of the majority of current desalting plants have been tested and found satisfactory. New equations covering a wide range of oil types are developed to estimate the optimum treating rate, treating temperature, and treating viscosity for dual polarity and AC conventional electrostatic desalters in Iranian oil fields. Based on these equations, this study suggests that dual polarity electrostatic desalters are more beneficial economically when compared with AC conventional electrostatic desalters. Dual polarity desalters may operate at a minimum crude oil inlet temperature, resulting in extra fuel savings, eliminating the need for a heating system in the vessel. Also, its treating rate is more than AC conventional electrostatic desalters.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".