Performance assessment and fast diagnosis of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.svg"> <mml:mi>μ</mml:mi> </mml:math> -CHP solid oxide fuel cell system
Bibliographic record
Abstract
Online performance characterization and faulty condition diagnosis of micro-combined-heat-and-power ( μ -CHP) solid oxide fuel cell (SOFC) system is crucial for ensuring safe in-house operation and lifespan prolongation. However, fast accurate detection of faulty conditions and performance characterization of compact SOFC system remains a problem. This study applied multiple diagnostic methods to characterize a μ -CHP SOFC system, including chronopotentiometry, electrochemical impedance spectroscopy (EIS), total harmonic distortion (THD) tool. Performance characterization and condition diagnosis were performed under different power demands, fuel starvation, high-carbon fuel feed and long-term operation. EIS results under normal conditions and fuel starvation showed that the bottom 27-cell half stack, located away from the fuel inlet, received less fuel compared to the top 30-cell half stack. Dispersion analysis indicated the safe fuel utilization for the system should be maintained below 81 % to avoid fuel starvation. THD measurements revealed that fuel starvation could be effectively detected by sinusoidal excitation with frequencies between 0.01 and 0.1 Hz, where a high THD index was observed. The carbon deposition behavior was examined by adjusting the carbon to oxygen ratio (COR) at the catalytic partial oxidation reactor inlet. With COR up to 1, no significant carbon deposition was detected according to the EIS measurements, system voltage and temperature monitoring. During long-term operation under normal conditions, no substantial degradation was detected, demonstrating stable and reliable power and heat generation. The novelty of this work included (1) the identification of operational variability between two half stacks, (2) the rapid detection of fuel starvation conditions using THD analysis, (3) performance assessment under high carbon fuel feed, and (4) long-term degradation characterization.
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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".