Energy, exergy, environmental, and exergoeconomic (4E) analysis of a fossil fuel power plant with carbon capture unit for a sustainable future
Bibliographic record
Abstract
The contribution of fossil fuels to the detrimental trend of global warming is undeniably real. Given the energy sector's heavy reliance on fossil fuels, it is crucial to take necessary actions to mitigate their adverse environmental impact. This study proposes a fossil fuel (natural gas) power plant integrated with a carbon capture unit (CCU) to minimize the associated environmental impacts. A thorough analysis of the system's energy, exergy, environmental, and exergoeconomic aspects (4E) reveals valuable insights into the proposed design. This CCU system captures CO 2 from the combined cycle power plant (CCPP) exhaust and employs it as a working fluid in the Supercritical CO 2 Power Cycle. The system generates 42.4 MW of power, 25.3 MJ/s of heat energy for hot water production, 49.6 MJ/s of heat energy for water desalination, 192.3 kJ/s for space cooling, and medium-pressure (MP) steam with a heat energy of 24.9 MJ/s. The system achieves 50 % energy efficiency and 54 % exergy efficiency, capturing 190.37 kilo metric tons of CO 2 per year. Additionally, the study examines the impact of variations in inputs, including air-fuel ratio, fuel quantity, ambient temperature, and steam temperature at the Heat Recovery Steam Generator (HRSG) outlet, on system efficiencies. 4E analysis reveals that the proposed system is more beneficial than simple power plants with no carbon capture units.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".