Energy, exergy and sustainability analyses of Bangladesh’s power generation sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it