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Production of Renewable Liquid Fuels by Coprocessing HTL Biocrude Using Hydrotreating and Fluid Catalytic Cracking

2021· article· en· W3213016376 on OpenAlexafffund
Yi Zhang, Anton Alvarez‐Majmutov

Bibliographic record

VenueEnergy & Fuels · 2021
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsFluid catalytic crackingHydrothermal liquefactionVacuum distillationHydrodesulfurizationGasolineChemistryDistillationCokePulp and paper industryChemical engineeringCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we explore coprocessing of hydrothermal liquefaction (HTL) biocrude with vacuum gas oil (VGO) in the fluid catalytic cracking (FCC) process. Coprocessing experiments were conducted using an advanced cracking evaluation FCC laboratory unit. Four sets of experiments were conducted: one with pure VGO to set the baseline performance and three sets with different VGO/HTL biocrude blends (5, 10, and 15% biocrude). Each set of tests covered a range of catalyst-to-oil ratios with temperature fixed at 510 °C. Prior to the FCC tests, the VGO and biocrude blends were hydrotreated in a continuous pilot plant to reduce the levels of heteroatoms, in an attempt to represent a refinery scheme with an FCC pretreat hydroprocessing unit. During the FCC tests, the biocrude blends showed lower conversion levels with respect to the baseline as a result of having more nitrogen and oxygen compounds that could have acted as catalyst inhibitors. Nevertheless, at a given conversion, the selectivity toward gasoline improved when the coprocessing ratio was 5%. The coprocessed gasoline products were nearly identical in terms of hydrocarbon type composition to the one from VGO at high conversion. The 10 and 15% biocrude blends showed a pronounced tendency to yield more light cycle oil, dry gas, and coke than VGO. As a whole, the study suggests that the coprocessing ratio for HTL biocrude should optimally be around 5% to minimize impacts on product yield distribution.

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 score0.929

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 head0.009
GPT teacher head0.212
Teacher spread0.202 · 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

Citations18
Published2021
Admission routes2
Has abstractyes

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