Decarbonization of cement production in a hydrogen economy
Why this work is in the frame
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Bibliographic record
Abstract
The transition to net-zero emission energy systems creates synergistic opportunities across sectors. For example, fuel hydrogen production from water electrolysis generates by-product oxygen that could be used to reduce the cost of carbon capture and storage (CCS) essential in the decarbonization of clinker production in cement making. To assess this opportunity, a techno-economic assessment was carried out for the production of clinker using oxy-combustion in a natural gas-fueled plant coupled to CCS. Material and energy flows were assessed in a reference case for clinker production (oxygen from air, no CCS), and compared to oxy-combustion clinker production from either an air separation unit (ASU, 95% O2), or water electrolysis (100% O2), both coupled to CCS. Compared to the reference, air-combusted clinker plant, oxy-combustion increases thermal energy demand by 7% and electricity demand by 137% for ASU and 67% for electrolytic oxygen. The levelized cost of oxygen supply ranges from $49/tO2 for an on-site ASU to pipelined electrolytic O2 at $35/tO2 (200 km) or $13/t O2 (20 km). The cost of clinker for the reference plant without CCS increases linearly from $84/t clinker to $193/t clinker at a carbon price of $0/tCO2 to $150/tCO2, respectively. With oxy-combustion and CCS, the clinker production cost ranges from $119 to $122/t clinker, reflecting a breakeven carbon price of $39 to $53/tCO2 compared to the reference case. The lower cost for the electrolytic supply of by-product oxygen compared to ASU oxygen must be balanced against the reliability of supply, the pipeline transport distance and the charges that may be added by the hydrogen producer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it