An open-source dynamic model for direct air capture of carbon dioxide using solid sorbents
Bibliographic record
Abstract
• We present a dynamic, open-source model for solid-sorbent direct air capture (DAC) • It integrates hourly, location-specific weather to predict realistic performance • Results reveal strong seasonal variation in capture performance • Energy intensity during hot periods is five times higher than during cold periods. • The model can support researchers in evaluating DAC performance in the real-world. Averting the worst consequences of climate change requires decarbonizing the global energy system and deploying carbon dioxide removal technologies, including the direct air capture of CO 2 . To estimate the cost and performance of the latter technologies, climate and energy system analysts need numerical process models that are validated with experimental data. Existing process models often limit reconfiguration that accommodates different design choices or restrict modelling to steady-state conditions. However, ambient environmental conditions like temperature, humidity, pressure, and inlet CO 2 concentration vary, affecting capture. This study develops an open-source process model for direct air capture using solid sorbents. Starting from first principles, this model allows users to select facility sizes, sorbents, other design parameters, and locations to simulate the capture performance of a solid sorbent direct air capture plant. More importantly, users can incorporate climate data to determine site-specific performance. Here, model validation is presented for two cold-climate sorbents that are being proposed for nations in northern latitudes. Results for climatically different cities are presented, highlighting the importance of sorbent choice and ambient environmental conditions on the overall capture performance and energy requirement of a direct air capture facility. The model can be employed by engineers, investors, and energy system analysts to undertake design optimization research, siting analyses, and improved studies that integrate high-fidelity process models into energy system optimization.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".