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Record W2794135719 · doi:10.1002/cjce.23143

Study on pyrolysis of oil sludge with microalgae residue additive

2018· article· en· W2794135719 on OpenAlexvenueno aff
Zhiqiang Gong, Zhentong Wang, Zhenbo Wang, Aixun Du, Peiwen Fang, Zhiqian Sun, Xiaoyu Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong Province
KeywordsResidue (chemistry)PyrolysisEnvironmental sciencePulp and paper industryWaste managementPyrolysis oilOil sludgeChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The pyrolysis of oil sludge (OS) with microalgae residue (MR) additive was conducted with a TGA and a tube furnace. The pyrolysis process of OS with the MR additive can be divided into three stages: 1) water evaporation, 2) the release of light groups of hydrocarbon compounds, the cracking of heavy groups, and carbon decomposition, and 3) minerals decomposition. With the MR addition ratio increasing, the yield of oil and gas increased, and oil to gas ratio increased during OS pyrolysis. The MR addition improved the quality of pyrolysis oil and gas from OS pyrolysis. The proportion of light oil increased from 38 % with a 5 % MR addition ratio to 45 % with a 30 % addition ratio. Major components of pyrolysis gas included H 2 , CO, CO 2 , and C x H y . With the increase of the MR blending ratio, CO and CO 2 contents increased, while H 2 and C x H y contents decreased. Adding MR favoured the transformation of heavy hydrocarbons (C 6+ ), resulting in a high content of light hydrocarbons. This work can help promote massive synergistic treatment of OS and microalgae biomass.

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 score0.482

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 head0.182
Teacher spread0.175 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

Explore more

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