Modelling crystal growth between potash particles near contact points during drying processes. Part II: Analysis, results, and comparison with experimental data
Bibliographic record
Abstract
Abstract Caking of bulk granular materials is a serious problem that affects many industries including the mineral processing industry. Caking occurs when bulk materials undergo wetting and drying cycles and it has been thought that it occurs due to the formation of new crystal bridges between individual particles. In the first paper in this series a mathematical model is developed for the crystal formation process that occurs at the contact point between two particles. In Part II, numerical simulations of the model are used to determine the effects of changes for several independent parameters in this model: initial moisture content; rate of evaporation of the salt solution from the particle surface; relative size of the contact region compared to the initial film thickness of salt solution; and supersaturation levels near the contact point. Non‐dimensional graphical curves of these simulations are used to compare the effects of each parameter for the deposition of salt crystals near the contact point. These results, when compared to data for cake strength in potash specimens which were obtained for various initial moisture contents, drying rate, and chemical composition of the particle surfaces, show good qualitative agreement even though cake strength and mass recrystallization near a contact point are different physical phenomena. The numerical results show that the mass of crystal deposition near the contact point will increase with increased initial moisture content and decreased evaporation rate. It is also found that variations in the degree of supersaturation near the contact point causes significant variations in the crystal mass deposition near the contact point.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".