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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".