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Record W4280550832 · doi:10.1016/j.jobab.2022.05.002

Evaluating process of auto-hydrolysis prior to kraft pulping on production of chemical pulp for end used paper-grade products

2022· article· en· W4280550832 on OpenAlexvenueno aff
Wenchao Jia, Miaofang Zhou, Chenfeng Yang, He Zhang, Meihong Niu, Haiqiang Shi

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Liaoning ProvinceNational Natural Science Foundation of China
KeywordsPulp (tooth)Pulp and paper industryKraft processHydrolysisKraft paperSoda pulpingPapermakingBlack liquorChemistryUltimate tensile strengthLigninMaterials scienceComposite materialOrganic chemistryDentistryEngineeringMedicine

Abstract

fetched live from OpenAlex

The objective of this work is to systematically evaluate the performance of the hydrolysis-based kraft pulping process and associated pulp and black liquor characteristics. Acacia wood chips were auto-hydrolyzed under various severities, then the hydrolyzed wood chips were kraft pulping. The results indicated that the yield of pulp significantly dropped with intensifying the auto-hydrolysis severity. Meanwhile, the removal rate of pentosan reached 98.6% in the screened pulp at the P-factor of 1 000. The fiber length, fines and fiber crimp of the screened pulp were not affected by the auto-hydrolysis treatment. Auto-hydrolyzed pulps deteriorated fibrillation and beating response of the pulp in a refining process. However, fiber length and fines changed obviously after beating treatment. After auto-hydrolysis, the tensile index of the paper matrices decreased, some particle substances were found on the surface of the pulp fiber, and the solid and organic content of the black liquor were improved.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.341
Teacher spread0.295 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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