Scaling considerations and optimal control for an offshore wind powered direct air capture system
Bibliographic record
Abstract
Abstract The optimal design and operation of an offshore wind powered direct air capture (DAC) system is complex owing to the intermittent energy supply and the modularity of the units. A solid amine DAC process involves multiple individual units which undergo periodic loading to capture carbon dioxide (CO2) from ambient air, followed by regeneration to produce pure CO2 for utilisation or sequestration. The modular nature of a solid DAC process is exploited in this study to investigate the optimal design and coordinated operation of multiple DAC units mounted on a single 15 MW offshore wind turbine platform, with battery energy storage for additional short term power buffering. Important design parameters considered include the number of independently controllable units, the cyclic capacity of each unit (proportional to the amount of adsorbent) and the battery capacity and maximum power ratings. The design study results highlighted the diminishing returns to the CO2 capture rate with scaling, with a full design optimisation based upon cost estimations left for future work as the technology matures. It was found the optimal configuration was 14 DAC units, each with a cyclic capacity of 2000 kg CO 2 , giving a total annual capture rate of 45 600 ton yr−1 and a wind utilisation factor of 96.6%. Furthermore, it was found that a rules-based control strategy based on high and low loading limits was competitive with a machine learning based controller and outperformed a model predictive control scheme.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".