Optimization of a Heliostat Field by Multiobjective Particle Swarm Optimization (MOPSO) Algorithm Based on Energy, Exergy, and Economic Point of Views
Bibliographic record
Abstract
Abstract This research is devoted to the energy, exergy, and economic analyses and optimization of a heliostat field. The model of the heliostat solar receiver includes detailed geometric factors related to the optical and thermal losses and efficiencies throughout the year. The main parameters of the thermal performance of this system consist of energy and exergy efficiencies, and economic parameters are investigated. By computing the energy, exergy, and economic analysis tools, they are applied for the analysis of performance, and viability of the system’s operating in Tehran City, including the detailed information of the environmental conditions of that location. For optimization purposes, 7 design variables related to geometric specification of the heliostat field are selected and the related lower and upper bonds are selected. Two target functions considered for the optimization are heliostat field exergy efficiency and payback period. The economic feasibility results of this study reveal that the net present value is 58.84 million US$, the payback period is 6.76 years, and the internal rate of return is 0.16. By considering the MOPSO algorithml, the annual mean exergy efficiency is increased from the 30.9–34.3% while the heliostat field payback period in reduced from the 6.76 to 4.3 years.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".