Gasified Biomass Fueled Hybrid SOFC Based Power Cycle: Impacts of Carbon Monoxide Fraction in Inlet Fuel
Bibliographic record
Abstract
One of the main challenges to use biogas as fuel in hybrid solid oxide fuel cell (SOFC) cycles is variable nature of biogas composition which may cause significant changes in plant performance. On the other hand, carbon monoxide is one of the main components in gasified biomass. Therefore, it is vital to investigate the influences of CO fraction in inlet fuel on the cycle performance. This paper presents the analysis of impacts of carbon monoxide concentration in inlet fuel on the performance of hybrid tubular SOFC and gas turbine (GT) cycle with two configurations, system with and without anode exhaust recirculation. The simulation results are considered when system is fueled by pure methane as a reference case. Then, the performance of the hybrid SOFC-GT system when methane is partially replaced by CO from concentration of 0% to 90% with an increment of 5% at each step is investigated. The proposed model is intended for steady state simulation of hybrid SOFC-GT cycle and is developed in Aspen Plus®. The system performance was monitored by investigating parameters like SOFC and system thermal efficiency; SOFC, GT, and cycle net and specific work; air to fuel ratio; as well as air and fuel mass flow rate. The results of the sensitivity analysis demonstrate that CO concentration has significant effects on the system operational parameters, such as efficiency and specific work.
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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) | 0.001 | 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) | 0.001 | 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".