Reduction in CO<sub>2</sub>emission and fuel exergy saving through cogeneration for sustainable development
Bibliographic record
Abstract
The depletion of non-renewable energy sources and its high cost, along with global warming related problems, have led engineers to re-assess more efficient and eco-friendly energy utilization.The objective of this paper is to determine the magnitude of fuel exergy saving and CO 2 emission reduction from combined heat and power plants in comparison to separate heating and power generation plants.The energy and exergy efficiencies have been defined for different types of energy plants.The efficiency of the cogeneration unit and the CO 2 emissions of the power plant are the two major factors that determine the amount of reduction in CO 2 emission.The expression for fuel exergy saving through cogeneration is also developed in terms of the second-law efficiency of cogeneration plants and the second-law efficiencies of separate heating and power generation plants.The exergy saving is determined for various kinds of cogeneration arrangements.From the results of the study, it is observed that maximum exergy will be saved in internal combustion based cogeneration plants and minimum exergy will be saved through extraction-condensing steam turbine based cogeneration.Results also show a similar trend for CO 2 emission reductions.It is observed that with the increase in efficiency of the power plant and cogeneration, the CO 2 emission decreases.A reduction in CO 2 emission ranges of 20%-25% is possible depending on the conditions.The proposed methodology may be quite useful in the selection and comparison of combined energy production systems in terms of CO 2 reduction, within the framework of the Kyoto Protocol on climate changes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".