An Exergy-Based Multi-Objective Optimization Of A Heat Recovery Steam Generator (HRSG) In A Combined Cycle Power Plant (CCPP) Using Evolutionary Algorithm
Bibliographic record
Abstract
In the present study, a heat recovery steam generator (HRSG) with a typical geometry and a number of pressure levels used at combined cycle power plants (CCPPs) is modeled. In order to validate the model results, they are compared with data obtained from the actual running power plant located near the Caspian Sea in Iran. The results show a good agreement between the model results and the experimental data. Upon a comprehensive exergy analysis conducted for this HRSG, the results show that an increase in the high and low drum pressures results in an increase in the HRSG exergy efficiency, while an increase in the pinch temperature leads to a decrease in the HRSG exergy efficiency. Also, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables is applied to obtain maximum exergy efficiency with minimum total annual cost per produced steam exergy as a two objective functions. The decision variables (or design parameters) are high and low drum pressures, steam mass flow rates, pinch point temperature differences, and the duct burner fuel consumption flow rate. The first objective function included capital or investment cost and operational cost and is minimized while satisfying a group of constraints, and HRSG exergy efficiency is maximized simultaneously. In addition, a regression analysis for curve fitting is conducted to correlate the data to determine the optimal points from the multi-objective optimization to predict the trend of each objective function. The results show that an increase in high pressure and low pressure drum pressure results in increasing HRSG exergy efficiency and also a smaller pinch temperature corresponding to a larger heat transfer surface area and more costly system, as well as higher exergy efficiency and lower operating cost.
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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 | 0.001 |
| 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 | 0.000 |
| 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".