Comparative Exergoeconomic Analysis of Waste Heat Recovery from a Gas Turbine-Modular Helium Reactor Using Organic Rankine Cycles
Bibliographic record
Abstract
A comparative exergoeconomic analysis is reported of waste heat recovery from a Gas Turbine-Modular Helium Reactor (GT-MHR) using various arrangements of Organic Rankine Cycles (ORCs) to produce electrical power. The organic Rankine cycles considered are: a simple Organic Rankine Cycle (SORC), an ORC with an internal heat exchanger (HORC) and a regenerative Organic Rankine Cycle (RORC). The exergoeconomic analysis is performed based on the specific exergy costing (SPECO) approach. For this purpose, the combined cycles are first analyzed thermodynamically from both energy and exergy perspectives. Then cost balances and auxiliary equations are applied to subsystems and exergoeconomic parameters are calculated for the components and the overall combined cycles. Based on fixed operating conditions for the GT-MHR cycle, the three combined cycles are compared. Finally a parametric study is performed to ascertain the effects on the exergoeconomic performance of the combined cycles of such significant parameters as compressor pressure ratio, turbine inlet temperature and evaporator temperature. The results show that the GT-MHR/RORC achieves the lowest unit cost of electricity produced by the ORC turbine, while the highest unit cost of electricity is observed for the GT-MHR/HORC. In addition, the exergy destruction cost rate is found to be highest for the GT-MHR/RORC and lowest for the GT-MHR/HORC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".