Hydrogen Production from Methane Thermal Pyrolysis in a Microwave Heating-Assisted Fluidized Bed Reactor
Bibliographic record
Abstract
Methane (CH 4 ) thermal pyrolysis is a promising, carbon dioxide (CO 2 )-free method for hydrogen (H 2 ) production, decomposing CH 4 into H 2 and solid carbon. This highly endothermic and energy-intensive process can sustainably be powered by microwave (MW) heating supported by renewable electricity. In this study, we investigated the efficacy of H 2 production and simultaneous carbon capture through CH 4 thermal pyrolysis in a lab-scale MW heating-assisted fluidized bed reactor (MW-FBR). We identified the effects of the thermal gradient between the solid and gas phases in MW-FBR on the process through direct comparison with a conventional heating-assisted fluidized bed reactor (CH-FBR) under identical operating conditions. Solid dielectric particles in MW-FBR reach high temperatures, creating favorable conditions for CH 4 thermal decomposition. We examined the influences of temperature (900–1065 °C), mean residence time (0.5–1.5 s), and inlet CH 4 molar fraction (0.2–0.5) on CH 4 conversion and H 2 selectivity. We obtained the highest CH 4 conversion of 23% and H 2 selectivity of up to 98% within the applied experimental conditions. Monitoring pyrolytic carbon byproducts showed that 90% of the produced pyrolytic carbon remained in the bed and captured by the fluidized particles. Comparative analysis between MW-FBR and CH-FBR at the same experimental conditions revealed that MW heating substantially outperformed conventional heating due to MW thermal effect, thermal gradient between solid and gas phases, and hotspots formation. On average, CH 4 conversion increased by 150%. Carbon capture efficiency improved by 70%. In addition, MW heating produced more graphitic carbon.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".