Combination of population balance equation and multi‐layer capacitor theory for modelling of an industrial Dual Polarity® electrostatic treater
Bibliographic record
Abstract
Abstract In the present study, an analytical model is developed to investigate the dehydration efficiency of a NATCO Dual Polarity® electrostatic treater at steady state conditions using population balance equations (PBE). To investigate the effect of frequency on dehydration performance and electric field strength between electrodes, a detailed electrical model is considered. In the developed electrical model, electrodes are virtualized as simple ideal capacitors, with emulsion acting as dielectric. Proving the model's accuracy, gathered industrial data were compared with simulation results. In addition, current frequency as the main electrical parameter besides power supply electrical potential was proved to be effective on the strength of the applied electric field, the electrical potential profile on electrodes, and the outlet water cut in the treated crude oil. The results indicated that increasing the current frequency from 50 to 500 Hz, by enhancing direct current field strength, reduces the water cut by 0.03%. Although high temperatures and frequencies increase the electrical conductivity of the emulsion, electrical power lost during the disconnection of the power supply on electrodes will be retrieved by increasing frequency. Finally, the performance of different electric fields on crude oil dehydration was compared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".