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Record W2790674409 · doi:10.5380/rber.v7i2.58263

EVALUATION OF THE ELEMENTAL COMPOSITION OF MUNICIPAL SOLID WASTE BOTTOM ASH: A NEW METHODOLOGY FOR SAMPLE PREPARATION

2018· article· en· W2790674409 on OpenAlexaff
Rodolfo Sbrolini Tiburcio, Andrea Carolina Gutierrez Gomez, Mauro Malpeli, Ana María Pereira Neto

Bibliographic record

VenueRevista Brasileira de Energias Renováveis · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsDiscovery Air (Canada)
FundersAgência Nacional de Energia ElétricaPetrobrasFundação de Amparo à Pesquisa do Estado de São PauloUniversidade Federal do ABC
KeywordsBottom ashMunicipal solid wasteElemental analysiscardboardScanning electron microscopeWaste managementOrganic matterEnvironmental scienceEnergy-dispersive X-ray spectroscopyComposition (language)Materials sciencePulp and paper industryChemistryFly ashEngineeringComposite material

Abstract

fetched live from OpenAlex

The disposal of municipal solid waste (MSW) to thermochemical treatments promotes the formation of ashes as a by-product, which constitutes an important role in the design and operation of energy recovery plants, as the ash can cause corrosion and fouling problems. In this sense, this work analyzed samples of bottom ashes from combustible fractions (organic matter, plastics, textiles, paper/cardboard/Tetra Pak® and sanitary waste) of MSW in natura from Santo André – SP, Brazil. For this, a new methodology for sample preparation was proposed to evaluate the elemental composition of the bottom ashes, for later analysis by Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDS). The obtained data showed a standard deviation of less than 10%, guaranteeing a greater reliability of the results and corroborating with standard deviations presented in the scientific literature. In addition, the elemental composition of the bottom ash was similar between the different combustible fractions evaluated. Keywords: MSW, Combustible fractions, SEM-EDS.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.375
Teacher spread0.277 · 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.

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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRevista Brasileira de Energias RenováveisSame topicMunicipal Solid Waste ManagementFrench-language works237,207