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Record W2582732200 · doi:10.1002/cjce.22796

Economic trends for temperature of sugarcane bagasse pyrolysis

2017· article· en· W2582732200 on OpenAlexvenueno aff
Edvan Vinícius Gonçalves, Fernanda Lini Seixas, Lindinalva Rocha de Souza Scandiuzzi Santana, Mara Heloísa Neves Olsen Scaliante, Marcelino Luís Gimenes

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFinanciadora de Estudos e ProjetosCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBagassePyrolysisBiocharTonneBiomass (ecology)Heat of combustionWork (physics)Fossil fuelEnvironmental sciencePulp and paper industryMathematicsProcess engineeringWaste managementChemistryCombustionEngineeringAgronomyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This work examines some aspects of sugarcane bagasse pyrolysis. Fixed bed pyrolysis was performed varying the final process temperature. Product yields and the variation of quality properties (calorific values) of bio‐oil, biochar, and non‐condensable gas were investigated. A mathematical model to simulate the trend of temperature that maximizes a marginal gain was developed considering the regression equations for yields and prices of products (related to market prices and calorific value of conventional fuels) and for the amount of biomass used to supply energy to the process. Two methodologies were adopted to calculate the optimum process temperature. The results showed very close temperatures (600 and 602 °C) for the best performing simulations of endogenous and fixed prices methods, with little difference between the yields of products, but with good variation between the marginal gains ($124.9/tonne and $129.8/tonne). Finally, the results of endogenous prices method for the fixed bed reactor were compared to the vacuum and fast pyrolysis reactors.

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 score0.003
Threshold uncertainty score0.440

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 science0.0010.000
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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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