Feasibility analysis for carbon capture and utilization in cement-concrete industries
Bibliographic record
Abstract
Worldwide CO2 generation is estimated to range between 25 and 30 gigatonne per year. Environmental specialists and legislators suggest that preventing even a small percentage of this CO2 from entering the atmosphere can help alleviate the emission-induced climatic changes. It is estimated that for the cement industry, each tonne of cement clinker generates approximately 0.8 tonne of CO2. Therefore, the mitigation of carbon dioxide has become a collective global challenge, where legislative efforts in many countries seek to mandate the recovery of flue gas CO2 in the near future. The concept of Carbon Capture and Utilization (CCU) presents a valid means towards the effective containment of CO2 in the cement industry and its long-term fixation through utilization in the concrete industry. Carbonation curing meets the criteria of less emissions and energy consumption by presenting proactive efforts towards sustainable construction practices in the cement and concrete industries. In this study, the basic concept of CCU in cement and concrete industry will be adapted to model cradle-to-grave scenarios for carbonation curing of concrete products, which are then compared to conventional steam curing to investigate the economic, technical, and environmental benefits. The energy consumption related to the different components of CCU, which include CO2 capture, compression, transportation and utilization, were optimized to yield the lowest cost of carbonation curing scenario. Given CCU's economic benefits and strength attributes, it is possible for cement and concrete industry to attain the levied regulated emission reductions, justify its environmental contribution and keep economical competitiveness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.008 | 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".