Evaluation of the economic and technological aspects of producing blue hydrogen via ethanol-steam reforming with carbon capture
Bibliographic record
Abstract
• Optimized H 2 production rate at 10 t/d via ethanol-steam reforming process. • Optimized reformer for treating 200 t/d feed: 0.18 m dia., 2 m length, 3.14 MW heat duty. • Achieved 95% CO 2 capture with absorber and stripper heights of 12.2 m and 5 m, and diameters of 1.22 m and 2.60 m. • Reduced hydrogen production cost to $3.5/kg, enhancing economic feasibility. • 16.6% IRR and $13M NPV over 6 years, indicating strong investment potential. An industrially relevant method for obtaining hydrogen from hydrocarbons without emitting carbon into the atmosphere involves ethanol-steam reforming followed by carbon capture. Herein, we present a detailed conceptual process using ethanol-stream reforming to produce blue hydrogen, integrated with a carbon capture plant, followed by a techno-economic analysis. In the first step, the Aspen plus-based simulation of ethanol-stream reforming reactions is performed to optimize the reforming reactor geometrical parameters for a 10 t/day of hydrogen production. Afterward, the carbon capture system was designed with a standalone absorber and stripper, which were subsequently integrated for solvent makeup calculation. Considering the target value of hydrogen production, the optimized reactor diameter and length were found to be 0.18 and 2 m, respectively, corresponding to reactant flow (200 t/day) and heat duty (3.14 MW) at optimal circumstances. Absorber and stripper packing heights of 12.2 m and 5 m, respectively, with column diameters of 1.22 m and 2.60 m are required to extract 95% CO 2 from the reformed product stream. The techno-economic analysis indicates that the cost of producing one kilogram of H 2 is $3.5. The computed internal rate of return is 16.6%, the discounted payback period is 6 years, and the net present value is $13 million.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".