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Record W2755770610 · doi:10.1002/cjce.23026

Thermogravimetric investigation of spent shiitake substrate to solid biofuel

2017· article· en· W2755770610 on OpenAlexvenueno aff
Renata Silva Viotto, Amanda Alves Domingos Maia, Fábio Minoru Yamaji, Leandro Cardoso de Morais

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsThermogravimetric analysisHemicelluloseBagasseCelluloseStrawMaterials scienceOxidizing agentPulp and paper industryChemistryWaste managementOrganic chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

In order to minimize environmental problems related to the generation and disposal of spent mushroom substrate (SMS) from mushroom cultivation activities, this study aimed to evaluate the thermal behaviour of the spent shiitake substrate (SSS) and its resulting biochar. Analyzing biochars produced at a temperature of 350 °C (BC350) was found to be that with the highest energy potential due to a reduced amount of resulting ash and nitrogen, and larger amounts of carbon, hydrogen, fixed carbon, as well as a higher calorific value, gravimetric yield, and gravimetric yield factor. SSS and BC350 were used in thermogravimetric and kinetic analyses. Thermogravimetric analyses were carried out at three heating rates, 10, 15, and 20 °C/min in an oxidizing atmosphere. Conversion steps relating to water loss and degradation of hemicellulose, cellulose, and lignin have been identified. The kinetic study was carried out by non‐isothermal and non‐isoconversional Kissinger method. The activation energies obtained were 110 and 136 kJ · mol −1 for the SSS and BC350, respectively, and are well within the range found for other lignocellulosic materials, such as bagasse and wheat straw. The pre‐exponential factor, in the order of 10 8 , indicated the presence of complex elements.

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 score0.001
Threshold uncertainty score0.444

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207