Decarbonization of cement production in a hydrogen economy
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".