Investigating the drying behaviour of clay-containing slurries
Bibliographic record
Abstract
Managing clay-containing slurries during drying process remains a persistent challenge in various industries. Despite challenges of drying clay-containing-slurries, limited information is available. The aim of this study is to explore the drying performance of slurries containing kaolin and bentonite and gain insights into the underlying drying mechanisms. The research presented integrates drying experiments, rheology measurements, settling experiments, zeta potential measurements, FTIR, TGA/DTA, and SEM analysis. Bentonite-containing slurries retained more moisture due to their high-water adsorption capacity, with higher bentonite percentages extending drying times. The addition of Ca2+ ions reduced moisture content by replacing Na+ ions with smaller Ca2+ ions, making the slurries less viscous. The addition of Ca2+ disrupted the gel-like structure of bentonite as confirmed by SEM and FTIR. In contrast, kaolin-containing slurries maintain lower moisture levels owing to the non-swelling structure of kaolinite. SEM showed the formation of agglomerates for kaolin when Ca2+ was added The addition of Ca2+ ions had a subtle impact on drying rates, despite a slight increase in slurry viscosity probably due to the agglomeration of kaolinite particles. Both slurries exhibited three drying phases: rapid drying due to high moisture, a moderate phase with reduced rates, and a final phase of slowed drying as tightly bound moisture was harder to remove. This paper demonstrated the significance of understanding the drying processes of clay-containing slurries to enhance the overall drying efficiency.
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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 | 0.001 |
| 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 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".