Research of Purification of Oil Sludge Waste Mixture Using Fe2(SO4)3 As A Coagulant
Bibliographic record
Abstract
During the treatment of industrial waste water (IWW) formed in technological processes at the Heydar Aliyev refinery at the plant's treatment facilities, a deposition of oil sludge waste mixture (OSWM) with various composition and properties takes place on the bottom of these facilities. The definition of composition and properties of OSWM is not the main purpose of this research work. But according to the information given in the technical literature, it can be noted that OSWM contains ~5-90% oil (or oil products), 1-52% water, 0.8-65% solid impurities, the density of oil sludge is 1.5-2 g/cm3, pour point from -3°C to +80°C, flash point from 35°C to 120°C. Not only in our country, but throughout the world the problem of deep (up to 100%) ecologically effective purification of this mixture from the oil products waste mixture (OPWM) has not been completely solved until now. Despite the use of various coagulants (AlCl3, FeCl3, polyacrylamides, polyaluminum silicate chloride, polyferric silicate chloride), the maximum efficiency of purification of OSWM from OPWM was ~95% when carrying out the purification process in several stages. We conducted a study of purification of this mixture samples taken from the input of “Alpha-Laval” facility, processing OSWM at the above-mentioned plant (from the area where OSWM was collected for processing) using Fe2(SO4)3 as a coagulant. The choice of this coagulant is explained by the fact that the purification process of OSWM is simple, economically beneficial, meets all environmental safety requirements, and is carried out at room temperature. Based on the results of the study, it was established that the highly effective purification of the OSWM sample using a solution of Fe2(SO4)3 as a coagulant initially depends on the choice of an extractant, the volume of a coagulant, mixing time, the development of special optimal conditions. Treatment of OPWM from the OSWM up to 96.87% was achieved. Optimal conditions specially developed for this consist of using 5 ml of a primary gasoline fraction which is cheaper than petroleum ether as an extractant, 25 ml of a 5% solution of Fe2(SO4)3 as a coagulant, 10 ml of H2SO4 as a flocculant and interval mixing of the mixture during 6 hours at room temperature..
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".