Understanding the behaviour of dodecylamine as a model cationic collector in oil sands tailings dewatering applications using a novel FTIR based method
Bibliographic record
Abstract
Abstract Tailings treatment technology is currently being developed at Syncrude Canada Ltd. (SCL) that involves adding a cationic surfactant (collector) to fluid fine tailings (FFT) in order to alter clay surfaces from hydrophilic to hydrophobic and promote faster dewatering. To understand the behaviour and effects of cationic collectors, a robust analytical method for measuring concentrations of aliphatic amine collectors in aqueous samples was developed. Dodecylamine (DDA) was used as the model collector molecule for this study. The method involves a liquid‐liquid extraction with dichloromethane (DCM) at basic pH followed by Fourier transform infrared (FTIR) detection of the primary amine N─H stretch of DDA at ~3382 cm−1. This method was successfully used to measure DDA in the matrices of recycled oil sands process affected water (RCW) and FFT. The optimized method was then used to understand the behaviour of DDA in a number of geochemical environments that could occur during tailings treatment. Analysis of the solubility of DDA in RCW found that the solubility of DDA increases with decreasing pH, while increasing temperature increased the solubility of DDA to a lesser degree. The solubility of DDA in RCW at its natural pH of 9.16 was found to be 4.22 mg · kg−1. Lastly, preliminary tests verified that FFT solids have a high relative DDA adsorption capacity and that the current standard DDA dosage used at SCL of 650 g/t, is effective as all the DDA was found tightly bound to clay surfaces with no excess DDA left in the water, even under acidic conditions.
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 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".