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Record W3021717194 · doi:10.1007/s11356-020-09041-3

Pyrolysis of fatty acids derived from hydrolysis of brown grease with biosolids

2020· article· en· W3021717194 on OpenAlexafffund
Mehdi Omidghane, Mattia Bartoli, Justice Asomaning, Lin Xia, Michael Chae, David C. Bressler

Bibliographic record

VenueEnvironmental Science and Pollution Research · 2020
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacsBioFuelNet Canada
KeywordsBiosolidsPyrolysisHydrolysisGreaseWaste managementChemistryPulp and paper industryEnvironmental chemistryEnvironmental scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The escalating generation of biosolids and increasing regulations regarding their safe handling and disposal have created a great environmental challenge. Recently, biosolids have been incorporated into the hydrolysis step of a two-step thermal lipid conversion process to act as water replacement in the production of renewable chemicals and fuels. Here, the hexane extract recovered from hydrolysis of biosolids, lipids from brown grease hydrolyzed using either water (control) or biosolids as a water replacement, was pyrolyzed at 410-450 °C for 2 h. The product distribution and composition were not significantly different when biosolids were used to hydrolyze brown grease instead of water. The liquid product consisted mainly of alkanes, alkenes, aromatics, and cyclic compounds similar to those in petroleum-derived liquid fuels. However, the use of biosolids as a water substitute resulted in a significant increase in sulphur content of the pyrolysate, which will necessitate processes to reduce the sulphur content before or after pyrolysis. Nevertheless, the pathways proposed in this paper are considered as potentially economically viable approaches to not only resolve the issues associated with disposal of biosolids but also to produce renewable hydrocarbons for fuel and chemical applications. Graphical abstract.

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 score0.020
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.236
Teacher spread0.218 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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