Innovative Wind-Powered System for Liquid Fuel Production: Integrating Carbon Capture and Hydrogen Storage
Bibliographic record
Abstract
As global temperature rises and underground resources decrease, governments are increasingly encouraged to develop alternative fuels and and to mitigate polluting sources. Power-to-liquid (PtL) technologies utilizing renewable energy provide an effective solution for hydrogen (H 2 ) production, carbon dioxide (CO 2 ) capture, and liquid fuel generation, contributing to the achievement of net-zero emission targets. An optimized PtL system with waste heat recovery reduces design complexity, capital cost, external heat dependence, and environmental impacts. This study introduces a novel PtL configuration for the production and storage of H 2 and CO 2 in the form of liquefied fuels, including liquid methane, liquid H 2, formic acid, and methanol. The wind-based PtL structure comprises various subsystems, including amine-based carbon capture and proton exchange membrane electrolysis for H 2 production. Storage subsystems encompass methanol synthesis, artificial methane production, formic acid generation, and liquefaction facilities. Pinch, exergy, economic, consequence, and optimization analyses are employed to evaluate the performance of the proposed system. The energy and exergy efficiencies of the hybrid processes are 59.91% and 50.97%, respectively. The economic analysis reveals a levelized cost of liquefied H 2 at 2.705 US$/kg and an investment return period of 6.505 y. Sensitivity analysis, machine learning, multiobjective optimization, and decision-support frameworks are utilized to determine the optimal operating conditions. The exergy efficiency, investment return period, and CO 2 absorption rate determined using the three-objective genetic algorithm and fuzzy approach from the Pareto front are obtained as 0.5404, 3.235 y, and 12,098 t/y, respectively. The consequences of small leakage, fixed-duration release, and complete rupture of the product storage vessels are analyzed, and safe distances are determined.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".