Thermoeconomic analysis and multi‐objective optimization of a solid‐oxide fuel cell plant coupled with methane tri‐reforming: Effects of thermochemical recuperation
Bibliographic record
Abstract
An 80-kW solid-oxide fuel cell (SOFC)-based power plant coupled with methane tri-reforming is proposed and analyzed from the viewpoint of thermoeconomics. In order to integrate the SOFC power generation section with the external reformer, part of the exhaust gases from the afterburner is recycled and utilized as a reformer agent in the reactor. The main challenge in performing the economic analysis is the determination of the costs associated with the tri-reforming process, in particular the reactor cost. To accomplish this, a mathematical procedure is suggested and applied for a fixed-bed reactor based on both the chemical equilibrium and chemical kinetics describing the tri-reforming process. The effects on system performance of important design parameters, including current density, SOFC operating temperature, and exhaust gas recirculation (EGR) percentage, are investigated. The results indicate that system performance is enhanced by using lower values of current density and higher values of SOFC operating temperature. In addition, considering the thermal and environmental performance of system as criteria, the use of EGR is not recommended. However, as the EGR percentage increases, the product unit cost decreases, making EGR advantageous from an economic perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".