Modeling the Maximization of Waste Heat Use in a Liquid Solvent Direct Air Capture Plant Through Hydrogen Production
Bibliographic record
Abstract
Direct air capture (DAC) of carbon dioxide is a promising technology to enable climate change mitigation. The liquid solvent DAC (LSDAC) process is one of the leading technologies being piloted. However, LSDAC uses a high-temperature regeneration process which requires a lot of thermal energy. Although current LSDAC designs incorporate pre-heat cyclones and a heat recovery steam generator to enable heat recovery, these do not maximize the use of the heat in the products of calcination. In this paper, a linear optimization model is developed to minimize energy cost in a LSDAC that is powered by renewable energy and natural gas. First, the material flow network is modified to include a heat exchanger (HX) and water supply to a proton exchange membrane (PEM) electrolyser. Mass and energy balance constraints are then developed to include the water flow as well as the energy balance at the PEM and the HX. Results show that about 911 tonnes of hydrogen could be produced over 336 hours of operation using a 136MW PEM. Further analysis reveals that hydrogen production is only prioritized if the value is higher than the cost of natural gas.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".