The impact of climate on solvent-based direct air capture systems
Bibliographic record
Abstract
Direct air capture (DAC) is increasingly seen as a critical technology to reach mid-century net-zero targets and limit climate change to well below 2 °C. While the commercialization of DAC technologies is being pursued by numerous companies, there remains remarkably little information on their performance under “real-world” conditions. In this paper, for the first time, we investigate the influence of temperature and relative humidity of air on the CO2 capture rate at the air contactor, overall energy requirement, CO2 capture efficiency, and levelized cost of liquid-solvent based DAC systems. We observe that the overall energy demand decreases from 11.1 to 8.3 GJ/tCO2 as the CO2 capture rate increases from 40 to 85 % and that high capture rates can only be achieved in hot and humid climate conditions. We observe that a CO2 capture rate of 75 % is only possible above 17 °C and 90 % relative humidity, and this drops dramatically at lower temperatures. It is also observed that water evaporation in the air contactor is highest at dry and low relative humidity, as expected. The sensitivity analysis showed that CO2 capture efficiency is relatively insensitive to climate conditions for the liquid-solvent based DAC plant. Lastly, the levelized cost of natural gas standalone scenario varies from $240/tCO2 to $409/tCO2, and this is more sensitive to temperature than relative humidity. The levelized cost of the natural gas stand-alone case is between 7 % and 10 % lower than that of an electric grid-connected case across all climate conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".