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Record W2114636054 · doi:10.1139/s07-052

Kinetic study and characterization of sewage sludge for its incineration

2008· article· en· W2114636054 on OpenAlexvenueno aff
Rosa Rodríguez, Daniel J. Gauthier, S. Udaquiola, Germán Mazza, Osvaldo M. Martı́nez, Gilles Flamant

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsIncinerationSewage sludgeThermogravimetric analysisCombustionPyrolysisDecompositionWaste managementEnvironmental scienceEnvironmental chemistryPulp and paper industryChemistryEnvironmental engineeringSewage treatmentOrganic chemistry

Abstract

fetched live from OpenAlex

The sewage sludge from San Juan, Argentina, was characterized in an attempt to improve the thermal treatment efficiency and to reduce the environmental impact of incineration. The ash content is about 50% dry basis, and its weight loss at 105 °C was high. Taking into account the moisture content, it required drying for an autothermal combustion, thus imposing an additional combustible for incineration. The sludge contains high concentrations of several trace elements, and all but Hg are two to fivefold more concentrated in its ash. Thermogravimetric analyses were carried out on dry samples of sludge in inert and oxidative atmosphere. Three peaks can be observed in all differential thermogravimetric curves during the organic matter decomposition. The pyrolysis curve is over the combustion curve and parallel to it up to high temperatures. A kinetic model of the sludge weight loss during its incineration, based on these studies and combining the multi-step pyrolysis and combustion processes, is proposed and discussed.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.176
Teacher spread0.170 · 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 designBench or experimental
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

Citations15
Published2008
Admission routes1
Has abstractyes

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