Synergistic utilization of blast furnace slag with other industrial solid wastes in cement and concrete industry: Synergistic mechanisms, applications, and challenges
Bibliographic record
Abstract
Granulated blast furnace slag (GBFS) is widely used in cement and concrete industries due to its excellent hydration properties. However, there is a huge capacity gap between the steel industry and the cement industry, and hence, the supply of GBFS can hardly meet the demand. At present, few studies have focused on the preparation of cementitious materials with GBFS-like properties, and a detailed summary of the mechanisms is lacking. This review summarizes the physical and chemical properties of GBFS and comprehensively discusses the hydration process in cement. In addition, the synergistic effects between GBFS and solid wastes (red mud, steel slag, gypsum and fly ash) were analyzed in detail. Based on the analysis of this work, there are four synergistic mechanisms among them. Moreover, a method for using solid wastes as raw materials to produce composite GBFS is proposed. It is beneficial to valorize various industrial solid wastes, promote cross-industry cooperation and alleviate the demand of the cement industry for high-quality GBFS. Although it is a theoretically possible method, there are still some problems that need to be solved, such as the lack of uniform quality and environmental standards. This work can provide useful advice for the preparation of composite GBFS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".