Electrokinetic-Induced Phase Separation of Petroleum Wastes: Evaluation of Oil Sediment Behavior and Solids Properties
Bibliographic record
Abstract
The disposal of the oily wastes represents a serious threat to the environment. The treatment of such oily wastes poses significant challenges considering their physical and chemical properties. This study focuses on advanced electrokinetic methods as a technology to treat oily sediments through phase separation. The study comprises four objectives which are intimately linked to achieve the overall objective of exploring the electrokinetic method for treatment of water-in-oil emulsions. The first objective is finding the most efficient oil phase separation when four different regimes of electric fields are applied (namely constant direct current (CDC), pulsed direct current (PDC), incremental direct current (IDC) and decremental direct current (DDC)). The second objective is to investigate the factors affecting the electrokinetic process and dewaterability of different types of oily sludge. The third objective is investigating the effect of nanoparticles and synthesized catalysts on the phase separation efficiency within the electrokinetic system. The final objective was to elucidate the various mechanisms underlying the separation of oil, water and solids by thermal analysis of treated samples. \nThe results showed that the extent and quality of phase separation depend on the regime of electrical current applied. The DDC and IDC regimes resulted in the most efficient phase separation of the oil sediments, and even incurred a highly resolved separation of light hydrocarbons at the top anode. \nX-ray photoelectron spectroscopy (XPS) analyses showed a decrease in the concentration of carbon from 99% in centrifuged samples to 63% on the surface of the solids treated by PDC. Wettability alteration studies showed an increase in the level of fine solids in the aqueous phase following electrokinetic treatment thereby enhancing the hydrophilicity of the solids. \nThe best performance of titanium dioxide (TiO2) nanoparticles and a synthesized catalyst reactors results were obtained with the synthesized catalyst as compared to the use of TiO2 nanoparticles. Activation energy emphasized the effect of additives on separation of phases and availability of oil. Hence, the synergistic effects of TiO2 or synthesized catalyst with electrokinetic treatment can lead to better phase separation and may reinforce the current applications of the electrokinetic method in treating oil sediments.
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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 | 0.000 |
| 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".