Effect of cement kiln dust, lime and fly ash on metal leaching characteristics of oil sands tailings from Alberta, Canada
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».