Low-Carbon Hydrogen and Methanol Production via Integrated Vacuum Swing Adsorption, Fuel Cells, and Carbon Capture: Exergy, Economic, and Optimization Insights
Bibliographic record
Abstract
Developing integrated hydrogen (H 2 ) production, purification, and storage processes with optimal thermodynamic, economic, and environmental performance is essential to achieving low-carbon emission targets. This paper develops a novel intensified process for the multiproduction of H 2, methanol, and power with the goal of low-carbon dioxide emissions. This hybrid structure includes a steam hydrocarbon reforming process integrated with a vacuum swing adsorption cycle, a methanol synthesis plant, a carbonate-based fuel cell unit, an amine solvent capture process, and a Rankine-based energy recovery system. The produced synthesis gas is simultaneously employed in H 2 separation, methanol synthesis, and high-temperature fuel cell systems. The power and waste heat generated by the high-temperature fuel cell are used to meet the energy demands of the hybrid process. The results of the thermodynamic analysis demonstrate that energy and exergy efficiencies are obtained at 0.5834 and 0.6076, respectively. The economic analysis based on the system’s annual cost method reveals that the levelized cost of the product is 0.0575 US$/kWh, with a payback period of 4.487 years. A machine learning algorithm combining mathematical modeling and neural network design is developed, with input and output data identified through sensitivity analysis. The multiobjective genetic algorithm is employed to derive the Pareto front representing the optimal conditions from energy, exergy, and economic perspectives. Multicriteria decision-making methods are used to determine the optimal operating conditions. The fuzzy solution output indicates that the energy efficiency, exergy yield, and levelized cost of the electricity for the designed process are 0.6143, 0.6371, and 0.0511 US$/kWh, respectively. The consequences of small leakage, fixed-duration release, and complete rupture of the methanol storage vessels are evaluated, and appropriate safe distances are recommended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".