Techno‐Economic Analysis of Glycerol Steam Reforming with Amine‐Based Carbon Capture for Blue Hydrogen Production: A Rate‐Based Kinetic Model Approach
Bibliographic record
Abstract
ABSTRACT This study outlines a comprehensive process design utilising glycerol‐steam reforming for an H2‐enriched gas stream, coupled with carbon dioxide removal via a chemical absorption system, followed by a techno‐economic analysis. The Aspen Plus economic analyser assesses the developed model, incorporating simulation results and literature data. Initially, the CO2 capture unit was planned with a standalone absorber and stripper, later integrated for solvent makeup calculation. Findings reveal that as catalyst loading increased from 5 to 50 kg, glycerol conversion and product molar fraction improved. For a targeted H2 production of 10 t/day, optimal reactor dimensions are 3.2 m diameter and 30 m length, corresponding to a reactant flow of 105 t/day and a 2.52 MW heat duty at stoichiometry conditions. To capture 95% CO2 from the reformed product stream, absorber and stripper packing heights of 12 and 7 m, respectively, with column diameters of 1.25 and 2.71 m are necessary. The production cost of H2 is determined to be $3.8 per kg, as revealed by the techno‐economic analysis. Calculated values for net present value, discounted payback period, and internal rate of return stand at $30 million, 5 years, and 25.0%, respectively. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".