Перспективные методы очистки дизельного топлива от воды и механических примесей
Bibliographic record
Abstract
The review of the modern industrial and experimental-industrial technologies of diesel fuel refinement from emulsified and dissolved water, as well as from solid insoluble particles is performed. The traditional methods of destabilization of the emulsions: gravity, centrifugal, electrical, chemical, coalescent methods are considered as well as modern complex technologies, including filtering of diesel fuel through porous polymer materials with new properties. On the basis of comparative analysis of various methods of diesel fuel refinement technologies of the domestic firm «DITO» (Moscow) and the canadian firm «FILTERVAK» were recognized as the most effective. The «DITO» technology involves the heating of fuel, its separation and homogenization under the action of centrifugal forces in the vortex apparatus and the subsequent filtration and stabilization. The method of «FILTERVAK» is a multi-stage purification system with the use of preliminary strainer-filter, input filter of cartridge or basket type, coalescent separators, filters of fine purification and regenerating filters if it is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".