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Record W2970454393 · doi:10.1680/jenes.19.00020

Energy Recovery from the Aged Waste in Saravan Dump Site, Rasht, Iran

2019· article· en· W2970454393 on OpenAlexvenueno aff
Mehran Karimpour-Fard, Sandro Lemos Machado, Hadi Hasanzadehshooiili

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsIncinerationHeat of combustionEnvironmental scienceTonneMunicipal solid wasteWaste managementEnergy sourceEnergy recoveryCoalEngineeringCombustionMathematicsEnergy (signal processing)Chemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.167
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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