Exergetic Optimization of Power Generation Systems
Bibliographic record
Abstract
With increasing global population and dwindling energy sources, generation of energy in a cost-effective manner with minimum waste and environmental footprint becomes one of the greatest challenges of our time.Thus, there is an increasing need for more capable and intelligent use of energy sources by incorporating enriched designs and optimized algorithms to enhance the sustainability of energy systems through innovative solutions.In this regard, the methods and tools for novel analyses and evaluation of energy systems to improve their efficiency and reduce their cost and environmental impact become imperative and require significant attention.Recently, exergy has become a new distinct discipline for system design, analysis, optimization, and performance evaluation; and its use has been expanded drastically.Many researchers and practicing engineers refer to exergy methods as powerful tools for analyzing, assessing, designing, improving, and optimizing systems and processes.Exergy analysis appears to be an important tool to industry in (a) addressing the impact of energy resource utilization on the environment and economics, (b) furthering the goal of more efficient energy resource utilization, (c) determining locations, types, and true magnitudes of wastes and losses, (d) revealing whether or not and how much it is possible to design more efficient energy systems by reducing the inefficiencies, and (e) providing a sustainable development as a result of sustainable supply of energy resources.Exergy analysis is a method that uses the conservation of mass and conservation of energy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".