Energy, exergy and sustainability analyses of Bangladesh’s power generation sector
Bibliographic record
Abstract
Ensuring sustainability in electrical power generation is a major concern in the modern world. Reducing energy depletion from power generation can reduce emissions and contribute to sustainability. Exergy analysis can be used to assess and optimize energy systems and thus can help achieve sustainability. In this analysis, energy and exergy utilization of Bangladesh’s utility sector is investigated based on data from 2007 to 2016. The overall energy efficiencies vary from 34.9% to 36.3% while the exergy efficiencies vary from 35.0% to 39.2% within this period. Thermal power plants are seen to have greater exergetic improvement potential than hydro power plants. To correlate between exergy and environmental sustainability, this study applies several exergetic parameters as sustainability indicators. It is found that the depletion number varies between 0.61 and 0.65 while the exergy sustainability index varies between 1.54 and 1.64. The relative irreversibility and lack of productivity are greater for gas operated power plants than other thermal power plants. The largest relative irreversibility is 0.90 while the largest lack of productivity is 1.72. The waste exergy ratio varies from 0.48 to 0.59 while the environmental effect factor varies from 1.35 to 1.68. Renewable power generation is found to have a higher sustainability than fossil fuel power generation. It is believed that current analysis can serve as a benchmark to help attain power generation sustainability.
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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 | 0.003 | 0.004 |
| 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) | 0.002 | 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".