Effect of Mixing Water Temperature on the Thermal and Microstructural Evolution of Cemented Paste Backfill in Underground Mining
Bibliographic record
Abstract
Cemented paste backfill (CPB) gains strength through the hydration of the binder constituent of the CPB, where mix temperature is a key influencing factor. Both rate of strength development and ultimate strength are influenced by the overarching temperature conditions in which the binder hydration occurs. This study investigates the influence of mixing water temperature on the thermal behaviour, hydration kinetics, and microstructural development of CPB using a combination of thermal finite element modelling, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). Five CPB mixtures were prepared, with water temperatures ranging from 5 °C to 50 °C, and tested under controlled conditions to isolate the effects of the initial thermal input. Results show that moderate mixing water temperatures (20–35 °C) optimize hydration and mechanical strength, while excessive temperatures (≥50 °C) increase the risk of thermal cracking due to generation of excessive heat. The thermal modelling results demonstrated that the highest temperatures were observed in the bottom section of the fill mass, in contact with the surrounding rock, where the combined effects of mix-generated heat and rock conduction were most pronounced. The 50 °C mix reached a peak internal temperature of 85.6 °C with a thermal gradient of 40.5 °C, while the 5 °C mix recorded a much lower peak of 55.7 °C and a gradient of 16.8 °C. These results highlight that higher mixing water temperatures accelerate early hydration reactions and significantly influence the internal thermal profile during the first 21 days of curing. Based on these findings, the design of paste plants can be improved by incorporating a heating/cooling system for the mixing water tank—firstly, to ensure the water temperature does not exceed 50 °C and secondly, to maintain water within an optimal temperature range, potentially reducing binder consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".