Development and analysis of electrified combined reforming of methane and CO2 based on induction and resistance heating
Bibliographic record
Abstract
• Electrified CRM using COMSOL explores induction and resistive heating designs. • Reactor performance is compared across induction wall and rod, and electric wire heating modes. • Wall heating offers the highest efficiency and fastest time to steady-state output. This article provides a comprehensive computational study of electrified combined reforming of methane (E-CRM) using induction and resistance heating methods. Three reactor models were compared: induction by a stainless-steel wall, induction by a stainless-steel rod inside the reactor, and resistance using an internal electric wire. CFD models are implemented using COMSOL Multiphysics, incorporating fluid flow, heat and mass transport, reaction kinetics, electromagnetic fields, and electric current physics. Our results showed that wall-based induction is the most efficient configuration compared to the other ones, with 94 % methane conversion at the minimum power requirement of 26.47 kWh/kg CH 4 converted and maximum heating efficiency of 30.6 %. Moreover, the time-dependent results showed that the wall induction configuration had the fastest response to reach the steady state condition in under 10 min. The scale-up of the system to an industrial-scale CO 2 reforming resulted in the induction-heated technology being able to reach a high level of heating efficiency, with reactor volume being reduced by nearly 44 % compared to conventional steam methane reformers. These results demonstrate the potential for electrification, particularly the use of induction heating for reactor walls, to be a scalable and efficient method for the sustainable production of syngas and methanol from CO 2 . Finally, an assessment of net CO 2 emissions as a function of grid carbon intensity emphasizes that substantial decarbonization is achievable when these technologies are integrated with low-carbon electricity sources.
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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) | 0.002 | 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".