Effects of Calcination on the Cementitious Activity and Pozzolanic Reactivity of Bayer Red Mud from Different Sources
Bibliographic record
Abstract
Red mud (RM) is a hazardous waste generated by aluminum production. It is difficult to utilize due to its high aluminum and iron oxide contents, high alkalinity, and large specific surface. Still, extensive research is underway to explore its potential as partial replacement for cement in concrete. Due to the differences in the physical, chemical, and mineralogical characteristics of bauxites from different sources, the associated RMs are also different. Some studies have reported that unless calcined, RM produced by the Bayer process has negligible pozzolanicity. However, the appropriate calcination temperature is not unique as it will depend on the RM mineralogical composition. Here, the calcination mineralogical composition nexus and its effect on RM pozzolanicity are investigated in three types of RM produced by Bayer’s process. The RMs were calcined at 600, 800, and 1000°C for 2 hours, and were used as 15 wt.% replacement for Portland cement in mortar mixes. One of the RMs exhibited pozzolanicity without calcination while another showed increased reactivity after calcination at 800°C. The underlying mechanisms are discussed, and it is concluded that no specific calcination temperature(s) can be recommended to activate every RM. Contrary to the findings of previous studies, one of the investigated RMs, used in its virgin form at 15 wt.% replacement for cement, exhibited noticeable pozzolanic activity and achieved over 94% of the compressive strength of the control specimen at 91 days. The calcination of the same RM, irrespective of the calcination temperature, reduced its pozzolanicity.
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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 | 0.001 | 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".