Novel Electrified Thermal Hydrogen Production Approaches
Bibliographic record
Abstract
In this study, we investigated four advanced electrification-based hydrogen (H2) production approaches, including direct Joule heating-assisted steam methane (CH4) reforming (DJH-SMR), microwave (MW) heating-assisted chemical looping dry reforming of CH4 (MWH-CLDRM), microwave heating-assisted CH4 thermal pyrolysis (MWH-MTP), and microwave heating-assisted low-density polyethylene (MWH-LDPE) pyrolysis. In the DJH-SMR, we employed an electrically conductive catalyst in a fluidized bed and achieved 96% CH4 conversion with H2/CO ratio of over 3 (CO is the carbon monoxide) at bulk temperature Tb of 900°C and steam-to-carbon molar ratio of 1. In the MWH-CLDRM, we leveraged MW irradiations to selectively heat magnetite (Fe3O4), achieved 97% CH4 conversion at Tb of 800°C in the fuel (reducer) reactor with an H2/CO ratio of 2, while suppressed coke formation. With the MWH-MTP, we demonstrated 23% CH4 conversion at Tb of 1065°C with 98% H2 selectivity, while capturing 90% of produced solid carbon. The MWH-LDPE pyrolysis over an iron-nickel-alumina catalyst at Tb yielded 72% H2 and 52% of carbon nanotubes. Obtained results highlighted the DJH-SMR's potential for H2-rich streams, MWH-CLDRM's suitability for Fischer-Tropsch applications, MWH-MTP's promise for CO-free H2 production when powered by renewable electricity, and MWH-LDPE pyrolysis's dual benefit of waste valorization and H2 generation. Each approach exhibits high performance and scalability. Key words. Electrified thermal hydrogen production, direct Joule heating, microwave heating, methane pyrolysis, chemical looping dry reforming of methane, plastic waste pyrolysis.
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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 | 0.001 |
| 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".