Heidelberg Materials Carbon Capture and Storage Demonstration Projects
Bibliographic record
Abstract
Carbon capture has historically been implemented in industrial sectors such as petroleum and coal-fired power generation. The application of carbon capture and storage (CCS) to cement kilns is more recent. Kiln applications entail significant differences and complexities compared to earlier use cases, including differences in flue gas makeup and process characteristics. Various pilot/demonstration projects are being conducted by Heidelberg Materials, one of the world’s largest integrated manufacturers of building materials and sustainable solutions. The CCS facility at Heidelberg Materials’ Norcem cement plant in Brevik, Norway, is on track to be the world’s first site to capture carbon emissions from clinker production at an industrial scale. This demonstration project will provide learnings and model processes for not only the cement industry but other sectors as well. North America’s first industrial full-scale CCS solution in the cement industry is taking place at Heidelberg Materials’ cement plant in Edmonton, Alberta, Canada, and Heidelberg Materials’ GeZero (Geseke Zero Emissions) project in Geseke, North-Rhine Westphalia, Germany, is expected to demonstrate the first full-chain, full-scale CCS project (from source to sink) in a remote inland location.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.018 |
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".