Energy Recovery from the Aged Waste in Saravan Dump Site, Rasht, Iran
Bibliographic record
Abstract
Non-recycled plastic, as a recent potential source of energy, has a greater calorific value compared with most waste components. In this study, municipal solid wastes of the Saravan dumpsite were characterised for energy recovery purposes based on their different components and respective calorific values. The Saravan dumpsite is the largest dumpsite in northern Iran. An extensive field investigation was conducted with more than 100 m of boreholes, and samples with different ages and compositions were collected. The weighted average composition of the studied waste was 56·83% organic material, 12·6% paper, 13·02% plastic, 3·93% wood and 3·08% textiles, with calorific values of 4332, 11 599, 23 175, 15 000 and 15 129·56 kJ/kg, respectively. Considering the studied composition, a weighted average global calorific value of about 8000 kJ/kg was estimated. Considering an incineration operation of 800 Mg/d (800 metric t/d) waste-digging-out process, power generation of about 5·63 MW/d was estimated. Hence, the Saravan dumpsite can be considered as a potential source for the production of alternative energy from a future waste-digging-out and incineration/gasification operation. As another interesting result, the net price of electricity production from the Saravan incineration power plant is calculated as US$0·185/kWh for a 5-year capital fund returning period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".