Investigation of the Mobility and Extraction Potential of Vanadium and Coupled Metals (Nickel and Lead) in Oily Sludge Matrix under Electrokinetic Conditions
Bibliographic record
Abstract
Ammar Badawieh, Ph.D. \nConcordia University, 2015 \n \nAbstract \nOily sludge is a viscous complex mix of hydrocarbons, water, metals, and suspended fine solids. This by-product’s persistent toxic composition poses serious environmental concerns, making its containment one of the biggest challenges facing petroleum industries. The main objective of this research was to monitor and trace target heavy metals (with particular focus on vanadium) mobilized in a petroleum sludge matrix under electrokinetic conditions. This exploratory study would facilitate furthering reclamation procedures, and presents the prospect of converting oily sludge into high quality added-value products. The research was carried out in three experimental and analytical phases. Phase 1 consisted of the formulation of adequate Upstream/Downstream petroleum waste, where three target metals, namely vanadium, lead and nickel were considered. In Phase 2, electrokinetic (EK) technology was used to separate valuable oily sludge components, and mobilize metals. A series of EK cells containing sole and mixed metals permitted investigating the synergistic and antagonistic effects of the three target metals (V, Ni, and Pb). Phase 3 focused on behaviour and mobility of metals in the separated matrices. In this phase, a combination of procedures including, Fourier Transform Infrared (FTIR) analysis, and X-Ray diffraction (XRD) were applied simultaneously. Rheological tests confirmed electro-demulsification and phase separation in oily sludge matrices. Furthermore, Ethylenediamine-Tetraacetate acid (EDTA), and Diisooctyldithiophosphini acid (Cyanex 301) were compared in the metal supercritical fluid extraction (SFE) process in order to enhance metals’ extraction from the oily sludge matrix. The results obtained in this research provide insight into the mobility of target heavy metals (V, Ni, and Pb) in an oily sludge matrix under EK treatment. Furthermore, vanadium was found to be an accelerator for the separation of oily sludge components under EK conditions. The results demonstrated excellent vertical and horizontal electro-separation of phases in the upstream cells. In the downstream oily sludge, the presence of non-polar solvents affected the separation process. However, in both upstream and downstream cells, metal mobility created interesting scenarios, such that metals accumulated in the specific areas of the matrix. This mapping of metals would permit on their further removal. This research leads to development of a new oily sludge management system (EK-SEF-Cyanex 301), which would not only help in the reclamation of sludge, but may also create a stream of revenue from the recovery of metals (particularly vanadium).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".