Crossflow flat solid oxide fuel cell (SOFC) semi-empirical modeling and the multi-fuel property based on a commercial 700 W stack
Bibliographic record
Abstract
Solid Oxide Fuel Cell (SOFC) technology has attracted vast attention for combined heat and power (CHP) application on low or zero carbon emissions because of its high efficiency and multi-fuel selectivity. Due to the high operation temperature, a commercial SOFC system requires a complex system thermal balance and stack self-heating ability which are dramatically influenced by the inlet temperature, reaction heat and thermal radiation of the stack. Multi-fuel selectivity is a further distinct advantage when considering the future variety of renewable fuel sources, such as ammonia (from animal farming), bio-methane (from agriculture), and the other hydrocarbons using carbon capture as an effective H2 carrier. However, the alternating between different fuel types may result in reduced power and the internal heat balance to change, which may further lead to the stack cooling down or over heated. An appropriate model with fast calculation processing capability is necessary to assess the internal changes before switching the fuel source and to inform further decision making to enable the control system to adapt to the change. In this paper, a cross-channel, flat SOFC semi-empirical model is proposed and validated by a commercial 700 W stack using real testing data to assess the accuracy of the simulation results. The SOFC model has multi-fuel ability which presents different properties and internal understanding for H2, NH3, and reformed CH4 through relevant charts (for feed, velocity, temperature, reaction rate, current, power, heat, efficiency). The power outputs for the different fuel sources, H2, NH3, and reformed CH4, are 721.0 W, 628.9 W and 679.7 W respectively when calculated at an air inlet temperature of 893 K. The modeling work was completed using Aspen Plus for SOFC system thermal balance simulations which determined the power output property for 813.3 W (H2), 744.5 W (direct NH3) and 740.7 W (reformed CH4) at the 2.0 times stoichiometric ratio of air feed, and CHP rate of 0.903 (H2), 0.787 (direct NH3) and 0.311 (reformed CH4). This work has the potential to contribute to the United Nation Sustainable Development Goal for Affordable and Clean Energy, Goal 7.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".