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Kraft pulping of softwood chips with mild hot water pre-hydrolysis to understand the effects of wood chip thickness

2022· article· en· W4297876549 on OpenAlexafffund
Jingqian Chen, Rodger P. Beatson, Kenny Tam, Paul Bicho, Heather L. Trajano

Bibliographic record

VenueBioResources · 2022
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsBritish Columbia Institute of TechnologyCanfor Pulp Products (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsSoftwoodKraft paperKraft processPulp (tooth)Kappa numberPulp and paper industryLigninCelluloseMaterials scienceSoda pulpingHydrolysisFiberHemicelluloseComposite materialChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Hemicelluloses consume alkali during kraft pulping and dissolve in the black liquor as a low energy fuel. Acidic pre-hydrolysis of softwood chips removes hemicelluloses but preserves cellulose content prior to pulping. This study compared mild pre-hydrolysis (140 °C) kraft pulping with conventional kraft pulping of commercial softwood chips at two H-factors for wood chips with thickness ranging from less than 2 mm to over 6 mm. The chip thickness less than 2 mm increased hemicelluloses oligomer yield and showed little influence on pulp fiber yield. However, the kraft pulp fiber length decreased 5.6%. Kappa number and fiber reject increased dramatically when chip thickness was greater than 6 mm. The detailed compositional analysis of kraft pulp and fiber quality analysis indicate that pre-hydrolysis followed by kraft pulping enhanced delignification with limited reduction of fiber length and width, and increased kinks. Strategic considerations for the integration of pre-hydrolysis into kraft pulping for future biorefineries were outlined.

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 score0.072
Threshold uncertainty score0.391

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 head0.005
GPT teacher head0.175
Teacher spread0.169 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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