Pilot‐scale experimental study on arc‐coupled microwave plasma‐enhanced pulverized coal ignition
Bibliographic record
Abstract
Abstract To ensure energy security and promote renewable integration, fuel‐efficient ignition strategies for coal are of practical importance. This study establishes a pilot‐scale arc‐coupled microwave plasma ignition system to improve the ignition performance of low‐volatile pulverized coal while reducing energy consumption. The effects of microwave power, arc power, coal fineness, and feed rate on the ignition process were systematically investigated. Flame temperature, spectral emissions, and stability were evaluated using thermocouples, optical fibre spectroscopy, and flame imaging. Results show that microwave energy enhances arc plasma excitation and promotes the formation of reactive species, thereby improving ignition stability and combustion activity. Compared to arc‐only discharge, the coupled arc–microwave discharge achieves higher flame temperatures under the same or even lower total power, demonstrating superior energy efficiency. Fine coal particles (R90 = 10%–20%) respond more sensitively to microwave enhancement under low arc power, favouring a ‘low arc–high microwave’ configuration. Conversely, coarse particles (R90 = 30%) require a ‘high arc–high microwave’ setup for reliable ignition. Increasing the feed rate results in a more stable, compact flame and better microwave coupling, whereas insufficient feeding leads to flame dispersion, temperature fluctuations, and wall heat loss. This study elucidates the synergistic interactions between coal, microwave, and arc plasma under multi‐parameter conditions, offering theoretical guidance and practical insight for plasma‐assisted ignition in complex coal combustion systems.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".