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Record W2741021064 · doi:10.5006/c2017-09434

Material-Biodiesel Compatibility – Survey of Industry Experience

2017· article· en· W2741021064 on OpenAlexaffabout
Yan Li, Natashah Zaver, Xin Pang, Muhammad Arafin, Sankara Papavinasam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCompatibility (geochemistry)BiodieselProcess engineeringWaste managementMaterials sciencePulp and paper industryEngineeringComposite materialChemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract The regulation in Canada requires that, starting from 2011, the renewable fuel content in diesel is at least 2%. Similar USA requirements resulted in the production of more than 1.5 billion gallons of biodiesel in 2009. Several other countries also produce large amount of biodiesel. Two surveys were conducted in 2011 to understand the industry knowledge and experience with respect to the use of biodiesel. This paper summarizes the salient features from these surveys. This paper additionally focuses on material-biodiesel interaction at various stages between the production of biodiesel and usage. The main findings from these surveys include, Based on the experience gained so far, no major incident of material incompatibility with biodiesel of concentration up to 5% has been reported.Incidents of corrosion and microbiological influenced corrosion have been experienced in the presence of accumulated water; however the incidences are not higher than those experienced in the presence of petroleum diesel alone.No new issues of high-temperature corrosion have been reported in using up to 20% of biodiesel under automotive operating conditions.Materials (both metals and non-metals) that are incompatible with raw biodiesel (B100) have been established and, to a certain extent, are avoided in handling fuels containing biodiesel.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.556

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)0.0010.000

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 head0.082
GPT teacher head0.311
Teacher spread0.228 · 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 designObservational
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

Citations1
Published2017
Admission routes2
Has abstractyes

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