Optimization of operation scheme with combined BOG treatment process in LNG terminals
Bibliographic record
Abstract
Two BOG treatment processes are adopted in the Tangshan LNG terminal of PetroChina, including the recondensation and highpressure compression based export processes. However, the LNG cold energy could not be fully used if only the high-pressure compression method is used for BOG treatment in the low export condition. The booster compressor has high operation power and large energy consumption.For this reason, the combined BOG treatment scheme of the Tangshan LNG terminal was studied through the combined numerical simulation and theoretical research with the ASPEN HYSYS software. Meanwhile, the combined operation scheme of the re-condenser and booster compressor was formulated to make full use of the LNG cold energy and reduce the operating power consumption of the booster compressor. The results show that the BOG flow has a great impact on the downstream LNG temperature. To control the downstream LNG temperature change of the re-condenser, the BOG flow should be controlled at first, with the auxiliary of BOG temperature and LNG header flow control. The combined operation scheme after optimization could reduce the power consumption of the compressor by 5%-20%, so that about 9.4×105 kW·h power can be saved annually, with an obvious optimization effect. Generally, the research results could provide a theoretical reference for the calculation of the combined BOG treatment process in the LNG terminal and the formulation of the operation scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".