Experimentation of a novel sequential moving-bed DAC System
Bibliographic record
Abstract
Solid sorbent Direct Air Capture (DAC) technologies face significant challenges due to the high energy demands associated with adsorption and regeneration phases. Overcoming these limitations is essential to improve the scalability and sustainability of carbon removal solutions. This study investigates the performance of a novel sequential moving-bed (SMB) DAC architecture that utilizes solid sorbent cells circulating through three distinct zones: adsorption, regeneration, and heat exchange, in order to increase system duty cycle and allow for heat recovery between sorption and desorption. The primary objective is to evaluate the energy and capture performance of this configuration in comparison to a conventional fixed-bed system, using the same sorbent under the same operating conditions. Compared to a conventional fixed-bed system, the SMB configuration increased CO 2 uptake by nearly 30% while reducing total energy intensity by more than 35%, to just over 1000 Wh/kgCO 2 . These gains are attributed to reduced sorbent working mass, lower pressure drops, and efficient heat recovery. The findings highlight the potential of the SMB approach to enhance the performance and energy efficiency of DAC systems, offering a pathway toward more sustainable and scalable carbon removal solutions. • CO 2 uptake increased by more than 30% with the sequential moving-bed process. • Energy intensity decreased by over 35% with the sequential moving-bed process. • Energy intensity reduced to near 1000 Wh/kgCO 2 with the sequential moving-bed.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".