Waste Concrete Valorization; Aggregates and Mineral Carbonation Feedstock Production
Bibliographic record
Abstract
Concrete is a major constituent of our world. Its contributes to building society but is also an important contributor to the global CO2 emissions. The combination of waste concrete recycling and greenhouse gas abatement is obviously an interesting approach. Mineral carbonation is the methodology that allows the use of calcium oxide within the concrete and transform it into carbonates with the CO2. Following previous results, carbonation experiments were performed using concrete paste extracted from a waste concrete sample after aggregate separation. The latter was performed after crushing and attrition followed by sieving to obtain three fractions. The coarser one composed of aggregates, the second of sand and the last, a fine powder of waste concrete paste (MCF). The MCF is then used in carbonation experiments in an 18.7 L stirred reactor with a diluted source of CO2 following previously optimized conditions. Different S/L ratios were experimented. The results show that 110 kg of CO2 can be stored per ton of MCF obtained after separation. Using the mass balance obtained from the experiments, an economic evaluation was performed on both aggregate separation and carbonation. While the first step can be profitable, using the MCF as a material for industrial flue gas abatement is less evident, both on the applicability and the feasibility.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".