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Understanding the Co-Pyrolysis Behavior of Indonesian Oil Sands and Corn Straw

2017· article· en· W2579985855 on OpenAlexaff
Zisheng Zhang, Hongfei Bei, Hong Li, Xingang Li, Xin Gao

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Ottawa
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsPyrolysisThermogravimetric analysisStrawRaw materialChemistryYield (engineering)Chemical compositionChemical engineeringResidue (chemistry)Organic chemistryPyrolysis oilLiquid fuelOil sandsPhenolsThermal decompositionPulp and paper industryMaterials scienceMetallurgyInorganic chemistryCombustion

Abstract

fetched live from OpenAlex

In this work, co-pyrolysis of Indonesian oil sands and corn straw was investigated to evaluate the potential synergetic effect. Thermogravimetric analysis was conducted to study the thermal decomposition behaviors of individual and blend feedstocks. Improved pyrolysis characteristics and higher conversion were observed, indicating a remarkable synergetic effect. Moreover, co-pyrolysis experiments were carried out using a fixed bed reactor. The results showed that the co-pyrolysis liquid product yield was increased, while the formation of solid residue was reduced, suggesting a higher conversion. The liquid product characterization by gas chromatography–mass spectroscopy also indicated the significant synergetic effect on the liquid chemical composition. Valuable phenols and alcohols were increased, while unstable aldehydes were decreased, suggesting the chemical interactions between two feedstocks during the co-pyrolysis process. The yield improvement and compositional variations of the co-pyrolysis liquid product were beneficial for its use as fuel and chemical feedstock.

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 score0.008
Threshold uncertainty score0.338

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 head0.031
GPT teacher head0.237
Teacher spread0.206 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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