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Record W2914670055 · doi:10.1002/bbb.1974

Potential synergies of drop‐in biofuel production with further co‐processing at oil refineries

2019· article· en· W2914670055 on OpenAlexaff
Susan van Dyk, Jianping Su, James D. McMillan, Jack Saddler

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2019
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiofuelHydrothermal liquefactionOil refineryAviation biofuelRefineryEnvironmental sciencePetroleumWaste managementBiomass (ecology)TonnePulp and paper industryBioenergyChemistryEngineeringAgronomy

Abstract

fetched live from OpenAlex

Abstract Drop‐in biofuels have been defined as functionally equivalent to petroleum‐based transportation fuels and are fully compatible with the existing petroleum infrastructure. They will be essential in sectors such as aviation if we are to achieve emission reduction and climate mitigation goals. Currently, ‘conventional’ drop‐in biofuels, which are primarily based on upgrading of lipids / oleochemicals, are the only significant source of commercial volumes of drop‐in biofuels. However, the necessary increased, future volumes will likely come from ‘advanced’ drop‐in biofuels based on biomass feedstocks such as forest and agriculture residues. Biocrudes / bio‐oils produced from lignocellulosic feedstocks using thermochemical technologies such as gasification, pyrolysis, and hydrothermal liquefaction need to be further upgraded to drop‐in biofuels. However, advanced drop‐in biofuels have been slow to reach commercial maturity due to significant technical challenges, high capital costs, and the challenge of generally lower oil prices. It is likely that the co‐processing of drop‐in biofuels with conventional petroleum refining could considerably reduce capital costs. Initially, co‐processing is likely to be established through the upgrading of conventional / oleochemical feedstocks (lipids). Lipids are readily available in large volumes (global production in 2017 was ~185 million metric tonnes) and can be more easily integrated into oil‐refinery processes. In contrast, lignocellulose‐derived biocrudes / bio‐oils are not yet available in significant volumes and are more complex to co‐process in a refinery. The likely strategies for co‐processing of oleochemicals (lipids) and bio‐oil and biocrude feedstocks based on different insertion points within the refinery infrastructure are discussed. © 2019 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.006
GPT teacher head0.190
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations196
Published2019
Admission routes1
Has abstractyes

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