Effect of cement kiln dust, lime and fly ash on metal leaching characteristics of oil sands tailings from Alberta, Canada
Bibliographic record
Abstract
The oil sands industry of Alberta generates huge amounts of tailings in a slurry form that typically require up to 40 years to consolidate in very large tailings ponds which are up to 150 m in height. Cement kiln dust (CKD), a byproduct of the ordinary Portland cement manufacturing process, as well as lime and fly ash, collectively referred to as geopolymers, may have the potential to reduce the tailings slurry consolidation period from 40 years, thus affecting the sustainability of such tailings facilities. However, first, it must be demonstrated that these geopolymers will also decrease the metal leaching from thickened tailings (TT) and mature fine tailings (MFT) from the oil sands industry. This study was focused on the use of geopolymers to reduce the environmental impact of TT- and MFT-tailings in the Alberta oil sands industry. Toxicity characteristics leaching procedure (TCLP) and static leaching test (SLT) was used to examine the leaching of metals from tailings, with the SLT test effectively mimicking the leaching process in the tailings ponds environment. Under non-acidic conditions corresponding to the SLT test results, iron concentrations with values of about 530-705 ppm were found to be lower than previous studies on oil sand tailing ponds (2400 ppm). Results showed that geopolymer amendment of TT and MFT significantly reduced the leaching of heavy metals. SLT tests showed that amendment of MFT with 7% CKD decreased Pb, Mn, and Fe leaching, whereas TT-amended with 4% CKD decreased Cu, Pb, Zn, Mn, and Fe. Overall, the CKD amendment of TT showed more than 95% efficiency in the reduction of leaching of all heavy metals. In TCLP tests, TT-amended with 2% FA decreased the leaching of Pb and Ni to acceptable levels with substantial efficiency in reducing the leaching of Fe, Cu, and Zn. TCLP tests also showed that among different amendments, TT-amended with 4% CKD or 2% FA were the most effective proportions for controlling metal leaching from TT, while MFT-amended with 7% lime/FA or 3% CKD were the effective proportions for reducing metal leaching from MFT. Thus, it is deduced that CKD at 3%-4% w/w amendment would work best for reducing leachate levels of both TT and MFT. While amendment of tailings by means of a combination of fly ash and lime also were effective in reducing the leaching of metals, these two geopolymers were not as effective as the CKD amendment.
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 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 | 0.000 | 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 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".