Aluminium migration through a geosynthetic clay liner
Bibliographic record
Abstract
Water treatment plants produce significant amounts of aluminium-rich residual solids (i.e. sludge) as a result of various processes to treat raw source water. Monofilling of these alum-based residual solids or other alum-rich sludge or leachates can be performed cost-effectively with geosynthetic clay liners (GCLs). However, there is currently a paucity of literature related to aluminium migration through GCLs and the influence of this aluminium migration on the engineering behaviour of GCLs. This paper presents results of GCL hydraulic conductivity, diffusion, and batch testing performed with aluminium sulfate solutions. Hydraulic conductivity test results show that modest increases in hydraulic conductivity with the aluminium solutions are observed after hydration with distilled water (k < 5 × 10−11 m/s). Diffusion testing with the same GCL has established an aluminium diffusion coefficient, Dt, of 1.5 × 10−10 m2/s and a linear distribution coefficient, Kd, of 30 ml/g. Batch testing performed confirms an initial high rate of aluminium uptake to the sodium bentonite, as well as non-linear behaviour. Adsorbed cation distributions on the bentonite and differential thermal analysis after 22 pore volumes of permeation with the aluminium solutions suggest that cation exchange and precipitation are mechanisms responsible for the attenuation observed as well as changes in hydraulic conductivity. An illustrative example is provided in the text to provide some perspective on the possible use of results presented herein for alum residual monofills.
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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) | 0.001 | 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".