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Catalyzed Hydrothermal Carbonization with Process Liquid Recycling

2019· article· en· W2913267024 on OpenAlexafffund
Amin Ghaziaskar, G.A. McRae, Alexis Mackintosh, Onita D. Basu

Bibliographic record

VenueEnergy & Fuels · 2019
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrothermal carbonizationChemical engineeringCarbonizationCarbon fibersCoalChemistryPelletsYield (engineering)PyrolysisBiofuelCatalysisMaterials scienceWaste managementOrganic chemistryComposite numberAdsorptionComposite material

Abstract

fetched live from OpenAlex

Catalyzed hydrothermal carbonization (CHTC) was used to produce hydrochar biofuel from wood chips at 240 °C in 1 h batches that included recycling of the process liquid. Infrared spectra showed changes in the chemical structure consistent with dehydration and decarboxylation. The CHTC hydrochar had higher heating values (HHV) of 28.3 MJ/kg, energy yield of 64%, and hydrogen-to-carbon (H/C) and oxygen-to-carbon (O/C) ratios similar to those of coal. The same process without the catalyst (HTC) produced a hydrochar with HHV of 27 MJ/kg, energy yield of 57%, and H/C and O/C ratios similar to those of lignite. Partial recycling of the CHTC process liquid resulted in a 5% increase in the energy yield; elemental composition, HHV, and scanning electron microscopic images of the CHTC hydrochar for different recycles were indistinguishable. Densified CHTC hydrochar pellets were 97% durable and hydrophobic when compared with wood pellets and torrefied-wood pellets, which was shown by water ingress measurements using an electrochemical cell with pellet electrodes. The CHTC process with recycling has the potential to provide a green hydrochar biofuel with excellent handling, storage, and transportation properties, that could be a suitable direct replacement for coal.

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 score0.009
Threshold uncertainty score0.553

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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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

Citations34
Published2019
Admission routes2
Has abstractyes

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