Analysis of the performances of a solid oxide fuel cell fed by biogas in different plant configurations: An integrated experimental and simulative approach
Bibliographic record
Abstract
Solid Oxide Fuel Cells (SOFC) are efficient, modular and fuel-flexible high temperature electrochemical devices. SOFC systems can be coupled to biogas from anaerobic digestion plants to obtain efficient and decentralized CHP systems, maximizing the valorization of biogas in virtuous waste-to-energy schemes. The main challenges for biogas-SOFC plants are related to performance stability and degradation at process level. In this work the performance and stability of an electrolyte supported SOFC single cell (100 cm2) fed with biogas mixtures derived from different integrated biogas-SOFC CHP plant configurations (hot/cold recirculation; UFf 65e85% - obtained from previous simulation work) has been analyzed with an integrated experimental and simulative approach. To support the experimental results, a chemical equilibrium model of the gas conversion processes coupled with the electrochemical conversion route is developed in MATLAB in order to simulate the gas composition at the anode outlet, which is compared and validated with experimental data obtained by Gas Chromatography (GC). Results show that suitable and stable cell performances are obtained while feeding the SOFC samples by biogas (720 e800 mV; 0.16e0.2 W/cm2 at 0.25 A/cm2) where the main performance losses are related to steam content - as well as other gas species, deriving from the pre-processing of the biogas. The gas composition and UFf simulation results show good correspondence with the GC data (error range <5% for the matrix gases and <10% for water) highlighting that the SOFC
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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 | 0.001 | 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".