Thermo-Economic Analysis of an Intercooled, Reheat and Recuperated Gas Turbine for Cogeneration Applications: Part II — Part-Load Operation
Bibliographic record
Abstract
The knowledge of off-design performance for a given gas turbine system is critical particularly in applications where considerable operation at low load setting is required. This information allows designers to ensure safe operation of the system and determine in advance thermo-economic penalty due to performance loss while operating under part-load conditions. In this paper, thermo-economic analysis results for the intercooled, reheat (ICRH) and recuperated gas turbine, at the part-load conditions in cogeneration applications, have been presented. Thermodynamically, a recuperated ICRH gas turbine based cogeneration system showed lower penalty in terms of electric efficiency and Energy Saving Index over the entire part-load range in comparison to the other cycles (non-recuperated ICRH, recuperated Brayton and simple Brayton cycles) investigated. Based on the comprehensive economic analysis for the assumed values of economic parameters, this study shows that, a mid-size (electric power capacity 20 MW) cogeneration system utilizing non-recuperated ICRH cycle provides higher return on investment both at full-load and part-load conditions, compared to the other same size cycles, over the entire range of fuel cost, electric sale and steam sale values examined. The plausible reasons for the observed trends in thermodynamic and economic performance parameters for four cycles and three sizes of cogeneration systems under full-load and part-load conditions have been presented in this paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".