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Record W2041366333 · doi:10.1139/s03-008

Preliminary investigation of the vacuum pyrolysis of bituminous roofing waste materials

2003· article· en· W2041366333 on OpenAlexvenueno aff
Abdelkader Chaala, C. Roy

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPyrolysisHeat of combustionWaste managementRaw materialPetrochemicalIncinerationKilnEnvironmental scienceMunicipal solid wasteAsphaltPulp and paper industryRefuse-derived fuelMaterials sciencePyrolysis oilCombustionChemistryOrganic chemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

The treatment of bituminous roofing waste materials by vacuum pyrolysis was tested in a pilot plant. The pyrolysis experiment was carried out batchwise at a temperature of 500°C and a total maximum pressure of 16 kPa. The amount of feedstock tested was 37 kg. Pyrolysis product yields were 52.3 wt.% oils, 31.2 wt.% solids, 14.4 wt.% pyrolysis gas, and 0.4 wt.% water. Losses were 1.7 wt.%. The pyrolysis oils have a gross calorific value of 42.7 MJ/kg and can be used as a heating fuel. The oil as a whole also represents a valuable material for petrochemical applications. The gas is rich in hydrocarbons and can be used as a make-up heat source (45.2 MJ/kg) for the thermal decomposition process. The solid residues exhibit a moderate calorific value of 17.5 MJ/kg and might find their way as a solid fuel in cement kilns. The nature of the minerals present in the solid residues, which amount to 49.7 wt.%, seems to be compatible with the cement composition. The vacuum pyrolysis technology is viewed as a potential solution to landfill and incineration of this waste construction material. Key words: construction, waste, shingles, bituminous, roofing, pyrolysis, fuels, environment, petrochemicals.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.233

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 head0.173
Teacher spread0.168 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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