Production of carbon negative precipitated calcium carbonate from waste concrete
Bibliographic record
Abstract
Mineral carbonation can contribute to climate change mitigation through the production of synthetic limestone (CaCO3) from calcium silicate minerals and gaseous CO2. Some carbonates, such as Precipitated Calcium Carbonate (PCC), have industrial applications and may provide sufficient economic incentive for sequestering CO2 should the product be a marketable commodity. Cement is a suitable source of calcium and can be recovered from waste concrete. The production of cement accounts for 9.5 % of global CO2 emissions, however up to 50 % of the manufacturing emissions can be mitigated at the end of the materials service life through mineral carbonation. The objective here is to sequester CO2 through the production of PCC via the recovery and carbonation of calcium from waste cement. The calcium is suitable for mineral carbonation and can be effectively leached using an acid (e.g. HCl). When calcium is completely leached, the solution will be slightly acidic and contain impurities such as iron and silica. The impurities can be removed by adding alkalinity prior to CaCO3 precipitation, via reaction with Na2CO3 in a separate reactor. The Na2CO3 is produced by the absorption of CO2 using NaOH, while the resulting NaCl solution is recycled via bipolar membrane electrodialysis. Overall transportation emissions and costs are reduced and the process could enhance current waste concrete recycling practices. Furthermore, the low carbon intensity of electricity generation in Eastern Canada allows for capture of 444 kg CO2 per tonne of PCC.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".