Comparison of Exergy Efficiency of Oxygen- and Air-combustion H2O Turbine Power Generation Systems
Bibliographic record
Abstract
This paper evaluates the thermodynamic characteristics of two proposed power generation systems (PGSs): one is a CO2-capturing PGS with regenerative cycle based on an oxy-fuel combustion method and the other is a PGS with a similar structure but which uses air to combust fuel. In each of the proposed PGSs, steam (H2O), which is produced by utilizing a heat energy resource outside of the system, is used as the main working fluid for a kind of gas turbine: this feature is different from a conventional gas turbine in which air is used. Exergy efficiency is used in evaluating the thermodynamic characteristics of the PGSs, since energy with different qualities, fuel and steam, are used as input energy in each system. It is estimated under assumed conditions that the oxygen-combustion PGS (OCS) has higher exergy efficiency than the air-combustion PGS (ACS). The reasons are as follows. The turbine outlet pressure of the air-combustion PGS is higher than that of the oxygen-combustion PGS. Additional energy is consumed for the air-combustion PGS; power to compress nitrogen gas included in the air for injecting it into a combustor, heat energy to raise the nitrogen gas to the turbine inlet temperature, and power to compress the condenser outlet gas to the atmospheric pressure for exhausting it to the atmosphere. For example, from the simulation study performed, the exergy efficiency of the OCS is estimated to be 54.4%, higher by 0.87%, compared to the highest exergy efficiency (53.8%) of the ACS. CO2 reduction characteristics of the two PGSs are also discussed. Key words: CO2-capture; Oxy-fuel combustion; Regenerative cycle; High efficiency; Simulation
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".