Thermodynamics fundamentals and energy efficiency for the separation and high-valued utilization of Fischer–Tropsch heavy oil
Bibliographic record
Abstract
Abstract The development trend of Fischer–Tropsch (F–T) technology is to develop high value-added products. The separation of linear α-olefins with low cost is an effective method. Nevertheless, the lack of thermodynamic data and the huge energy consumption are the two main problems restricting the development of the separation process. The thermodynamic data of the key components (1-dodecene and n-dodecane) in the F–T product were measured. The Wilson binary interaction parameters of the key components were obtained. Next, one traditional distillation column sequence and two dividing wall column (DWC) sequences were designed to separate the F–T heavy oil to obtain narrow fractions with different carbon numbers. Then, the obtained fractions of C10 and C12 were simulated to obtain 1-decene and 1-dodecene, respectively. There was a traditional distillation and a differential pressure thermal coupling distillation process. When separating 95.0% purity 1-decene and 1-octene, the direct DWC process and differential pressure thermal coupled distillation are an excellent combination, which can reduce the energy by 33.1% (i.e., 11,286 kW) and total annual cost by 15.9% (i.e., 3.96 × 106 $) compared with traditional distillation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".