NRG COSIA Carbon XPRIZE: carbon-dioxide mineralization in recycled concrete wash water
Bibliographic record
Abstract
Abstract Wash water is generated as a waste stream from ready-mixed-concrete production. Reuse of the water as mixture water is limited, in practice, by the negative material performance impacts associated with the water chemistry and properties; the effects are intensified with increasing content of suspended solids and age. However, this waste material can be used as a beneficial additive to concrete by profiting from the cementitious properties of the suspended solids, if variability can be reduced. A method of stabilizing this material is through CO2 treatment. The added CO2 is mineralized through a reaction with the calcium from the cement particles. This provides a calcium-carbonate coating that prevents further cement hydration, making the material predictable. This has been shown to alleviate concerns with set acceleration and inconsistencies in compressive strength. A method of CO2 treatment was tested as part of the NRG COSIA Carbon XPRIZE at a site in Calgary, Alberta. The slurry for the treatment was provided by a local concrete plant and had a specific gravity of 1.15. The simulated wash water was treated in 1000-L quantities with each treatment mineralizing an average of 40 kg of CO2. The system ran for 1600 hours of operation over 127 treatment cycles and converted 14.5 tonnes of CO2 at an average mineralization efficiency of 80%. The treated slurry was used as an additive in >300 batches of concrete where the concrete met the necessary requirements for fresh properties and setting time, while achieving a strength benefit. Replacement of 5% and 10% of batch water with treated slurry (9.4 and 18.8 kg slurry/m3 concrete) showed a strength benefit of 3% and 6% compared to a reference. The technology was selected as the winner of the NRG COSIA Carbon XPRIZE (Track B: Natural Gas) in April 2021.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".