Biomass electrostatic precipitator fly ash treatment methodologies to maximize cement replacement in mortar and concrete
Bibliographic record
Abstract
The dwindling resources of conventional supplementary cementitious materials (SCMs), such as coal fly ash and blast-furnace slag, have spurred considerable research into alternative SCMs. Landfilled at a high environmental and economic cost, biomass precipitator fly ash (BFA) is a major byproduct of burning biomass as a renewable source of energy. Despite interest in BFA as an alternative SCM, its inherent properties have limited its use to less than 10 % of cement weight. This comparative study aims to maximize the cement replacement ratio by BFA through various treatment methodologies. The treatment methodologies include calcination (i.e., 350 – 1000°C), milling and combined calcination and milling. The compressive strength and workability of the mortars indicate that two hours of calcination at 800°C is the optimal calcination treatment. Various BFA contents have been adopted to replace cement (i.e., 0 – 50 %). The influence of water/binder (W/B) ratio on calcined and untreated BFA mortars are also studied by considering W/B of 0.5 and 0.35. The results suggest that a 28-day compressive strength of 42 MPa can be reached by replacing 50 % of the cement with calcined ash, which satisfies the requirements of most engineering structures. As such, the cement replacement ratio with BFA, which has traditionally been limited by its low strength and workability, can be increased substantially through simple calcination. A conservative life-cycle carbon dioxide emission calculation indicates a 47.5 % reduction in CO 2 emissions with 50 % replacement of cement with treated BFA, and a tenfold reduction on a per-kilogram basis.
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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 | 0.000 |
| 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) | 0.001 | 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".