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Production and characterization of hydrochar produced from hydrothermal liquefaction of pipeline-transported softwood biomass

2025· article· en· W7081983613 on OpenAlexafffund

Bibliographic record

VenueBiomass and Bioenergy · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Alberta
FundersFusion Energy SciencesAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada First Research Excellence FundUniversity of Alberta
KeywordsHydrothermal liquefactionSoftwoodBiomass (ecology)Leaching (pedology)Carbon fibersLignocellulosic biomassBioenergyYield (engineering)LiquefactionLignin

Abstract

fetched live from OpenAlex

This study investigates hydrochar production from hydrothermal liquefaction (HTL) of pipeline-transported softwood biomass. Pipelining biomass using water as a carrier fluid offers a cost-effective alternative to truck transport, and its integration with HTL for hydrochar production is relatively unexplored. The objective of this study was to evaluate the effect of process parameters on HTL of pipelined biomass and characterize the resulting hydrochar for various applications. The yield of hydrochar produced from pipelined biomass, at the optimal process conditions, was 13.22 % (oven-dry mass basis), with 19.31 % of energy recovered in hydrochar. The hydrochar was characterized as a black, fragile, mildly acidic carbon material with a dry bulk density of 0.142 g/cm 3 , improved carbon content, increased surface area (9.27 m 2 /g), altered pore morphology, and a graphitized, amorphous structure, reflecting structural and compositional changes from the HTL process. Pipelining was found to have minimal impact on the lignocellulosic composition and particle size of the softwood biomass, but it resulted in the leaching of inorganic elements, leading to a reduction in ash content in the hydrochar. The characterization results suggest the hydrochar is suitable for soil amendment and solid fuel applications; its low surface area hinders its use as an adsorbent or for energy storage. This study thus demonstrates the potential of integrating hydro-transportation with HTL, offering a pathway to recover, manage, and use hydrochar for various applications. • The study characterized hydrochar from HTL of biomass transported via pipeline. • Hydrochar yield was 13.22 % with 19.31 % energy recovery under optimal HTL processing. • Hydrochar was black, fragile, and mildly acidic with 0.142 g/cm 3 dry bulk density. • Hydrochar had 72.4 % C, 9.27 m 2 /g surface area, and graphitized-amorphous structure. • Hydrochar obtained is suitable for soil amendment and solid fuel applications.

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 score0.031
Threshold uncertainty score0.404

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 head0.204
Teacher spread0.196 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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