Multi‐objective performance optimization of irreversible molten carbonate fuel cell–Stirling heat engine–reverse osmosis and thermodynamic assessment with ecological objective approach
Bibliographic record
Abstract
Abstract This paper aims to investigate a hybrid cycle consisting of a molten carbonate fuel cell (FC) and a Stirling engine which, by connecting to a seawater reverse osmosis desalination unit, provides fresh water. First, a parametric evaluation is performed to study the effect of some key parameters, including the current density and the working temperature of the FC and the thermal conductance between the working substance and the heat reservoirs in the Stirling engine, on the objective functions. The objective functions include the energy efficiency, the exergy destruction rate density, the fresh water production rate, and the ecological function density. After investigating each double combination of these objective functions, two scenarios are defined in quest to concurrently optimize three functions together. The first scenario aims to optimize the energy efficiency, the exergy destruction rate density, and the fresh water production rate; and the second scenario attempts to optimize the energy efficiency, the fresh water production rate, and the ecological function density. A multi‐objective evolutionary algorithm joined with the nondominated sorting genetic algorithm (NSGA‐II) approach is employed to obtain Pareto fronts in each case scenario. In order to ascertain final solutions between Pareto fronts, three fast and robust decision‐making methods are employed including TOPSIS, LINMAP, and Fuzzy. Finally, a sensitivity analysis is conducted to critically analyze the performance of the system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".