Hydrogen Production from Natural Gas Using Hot Blast Furnace Slag: Techno-economic Analysis and CFD Modeling
Bibliographic record
Abstract
Abstract A process for thermal decomposition of methane to hydrogen and solid carbon is presented and examined. It utilizes the high-temperature heat from the slag by-product of blast furnace ironmaking to drive a thermal decomposition reaction, making it a waste-heat-to-hydrogen technology. This is accomplished via dry granulation of molten slag that feeds a fluidized bed reactor to effect methane–slag contact. First, the proposed process and the heat and mass balances are presented. It is found that it could produce an amount of hydrogen that is equivalent to about 20% of the reductant, depending on the iron-to-slag ratio. Then, a techno-economic analysis investigates the capital and operating costs of the process, compares the hydrogen production cost to that of other processes, and examines cost sensitivity to the prices of process inputs and outputs. This analysis suggests that the process would be suitable for on-site hydrogen production and use within a plant. In addition, using the hot slag to drive the methane decomposition would reduce hydrogen production cost by 15% compared to combusting a portion of the natural gas itself. Finally, a computational fluid dynamics (CFD) modeling study of the fluidized bed reactor examines the thermal decomposition of methane and its dependence on reaction kinetics as well as reactor design and operation. The bed operated in the bubbling regime at an average temperature between 1020 and 1060 °C and resulted in as high as 82% conversion of the methane to hydrogen, with additional optimization still possible. Graphical Abstract
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".