Optimizing electricity production from food waste: A case study of Bangladesh
Bibliographic record
Abstract
• Anaerobic WtE system proposed for Dhaka, Bangladesh. • Uses 72.25% biodegradable MSW from Amin Bazar landfill. • 40 MW biogas plant modeled in HOMER Pro software. • LCOE of 8.7 Tk/kWh, cheaper than current power sources. • Cuts GHG emissions to 1,143 tons CO₂, 1.15 tons NOₓ yearly. Rapid urbanization in developing nations has intensified municipal solid waste (MSW) generation, posing critical challenges to sustainable urban development and energy security. This study presents a comprehensive techno-economic and environmental evaluation of an anaerobic digestion-based waste-to-energy (WtE) system tailored for Dhaka, Bangladesh-where over 72.25% of MSW is biodegradable. A 40 MW biogas power plant was modeled using HOMER Pro software, incorporating load profiles, grid interaction, and system cost dynamics. The proposed system achieves a competitive levelized cost of electricity (LCOE) of 8.7 Tk/kWh ($0.0733), significantly outperforming conventional Independent Power Producers (14.62 Tk/kWh), rental and quick rental plants (12.53 Tk/kWh) and imported power (14.02 Tk/kWh). Annual GHG emissions were reduced to 1,143,159 kg CO₂ and 1,150 kg NOₓ, compared to 638,442 tons CO₂e from open dumping, as quantified using the SP1 methane emission model. These findings establish anaerobic digestion as a scalable, low-carbon alternative for urban energy systems in resource-constrained settings, aligning with circular economy and climate resilience goals.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".