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Record W2789476516 · doi:10.21967/jbb.v3i1.123

Effect of wet storage on alkali-oxygen pulping of bagasse

2018· article· en· W2789476516 on OpenAlexvenueno aff
Zhen Shang, Bing Sun, Yuxin Liu, Li Bao

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2018
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsBagasseSoda pulpingPulp (tooth)Pulp and paper industryKappa numberCrystallinityHemicelluloseChemistryLigninAlkali metalCelluloseOxygenWaste managementMaterials scienceOrganic chemistryKraft processDentistryMedicineKraft paper

Abstract

fetched live from OpenAlex

Carbohydrate degradation is a serious problem in alkali-oxygen pulping due to the inherently oxidative reaction conditions. Hemicellulose and short-chain cellulose are particularly susceptible to degradation by oxidation, and thus have a great effect on the pulping yield, as well as the viscosity and crystallinity of resultant pulp in alkali-oxygen pulping. Removal of the low molecular weight substances by wet storage prior to alkali-oxygen pulping may increase the pulping yield and improve pulp properties, in addition to savings in pulping chemicals and energy. This paper investigated alkali-oxygen pulping of bagasse pretreated by wet storage, and results show that wet storage of bagasse had a significant effect on its alkali-oxygen pulping, in terms of pulping yield, pulp viscosity and crystallinity. The pH and time were found to be the two most important factors in wet storage on bagasse. Pulp crystallinity increased from 31.78% to 42.06% when the wet storage of bagasse was performed at pH 6.0 for 16 days. In addition, the screened pulp yield increased from 58 to 60%, and pulp viscosity increased from 650 to 700 mL/g. The improved pulping performance was attributed to increased pore volumes in bagasse due to dissolution of low molecular weight lignin and carbohydrates during wet storage, which in turn improved the selectivity of delignification in the alkali-oxygen pulping process.

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 score0.003
Threshold uncertainty score0.341

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 head0.204
Teacher spread0.200 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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