An efficient waste management system with municipal solid waste incineration plant
Bibliographic record
Abstract
Abstract This study proposes a waste management system for metropolitan cities and presents a thermodynamic analysis and emission assessment of a municipal solid waste incineration plant operating with collected wastes from the 37 municipalities of Istanbul city, Turkey. A thermodynamic analysis of the proposed system shows that 53.72 MW of electricity generation and the heat of 99.4 MW for district heating are obtained with the municipal solid waste incineration. The energy and exergy efficiencies of the plant are 55.5% and 35.4%, respectively. The rate of exergy destruction for each component in the plant and their variation with ambient temperature is calculated. The maximum rate of destruction occurs for the combustion chamber as 44.02 MW. According to the emission assessment, total CO2 emission is 45 500 ton per year and this value can be reduced to 4550 ton per year with the addition of carbon capturing and storage unit. The other released emissions from the incineration plant such as CH4, N2O, CO, NH3, and non‐methane volatile organic compounds (NMVOCs) are found to be 0.15, 34.36, 201.03, 16.08, and 20.10 ton per year, respectively. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 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".