Enhancing Cement Grinding Efficiency: Performance of Combined Polycarboxylate Ether and Triethanolamine Admixtures
Bibliographic record
Abstract
Grinding is one of the most energy-intensive and costly processes in cement production, consuming nearly two-fifths of the total electrical energy.To enhance efficiency and mitigate environmental impacts, including greenhouse gas emissions and energy waste, grinding aids (GAs) are widely utilized, with amine-and glycol-based additives being the most common.While these additives enhance grinding efficiency and cement properties, they can negatively affect setting time and fluidity.Polycarboxylate ether-based waterreducing admixtures (PCEs) have emerged as promising alternatives due to their similar mechanisms of action.Studies indicate that PCEs can achieve comparable grinding efficiencies to conventional GAs, and their combination with triethanolamine (TEA) offers further performance benefits.This study investigated the time-dependent grinding efficiency of cement when TEA and PCE were used individually and in combination.A Bond ball mill was used for grinding experiments, with TEA, PCE, and a combined P-TEA additive (PCE and TEA in a 1:1 ratio) added at 0.05% of the total clinker and gypsum weight.Blaine fineness values (cm/g) were measured after 2000, 4000, and 6000 grinding cycles.All GA types improved Blaine fineness compared to control cement, confirming their effectiveness.Among them, the P-TEA combination exhibited the highest performance, demonstrating a synergistic effect between PCE and TEA.These findings highlight the potential of combining PCE with traditional GAs to optimize grinding efficiency and cement performance.The superior results achieved with P-TEA suggest that tailored formulations integrating PCEs with conventional GAs could enhance both grinding efficiency and cementitious properties, contributing to more sustainable cement production.
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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".