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Acidification of prehydrolysis liquor and spent liquor of neutral sulfite semichemical pulping process

2016· article· en· W2474803197 on OpenAlexafffund
Pedram Fatehi, Weijiue Gao, Yonghui Sun, Mehdi Dashtban

Bibliographic record

VenueBioresource Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Safety and Security CommissionFPInnovations
KeywordsBlack liquorChemistryKraft paperDissolving pulpPulp and paper industryKraft processSulfiteLigninPulp (tooth)Soda pulpingCelluloseOrganic chemistry

Abstract

fetched live from OpenAlex

Acidification has been commercialized for producing kraft lignin from black liquor of kraft pulping process. This work intended to evaluate the effectiveness of acidification in extracting lignocelluloses from the spent liquor of neutral sulfite semichemical pulping (NSSC) process and from prehydrolysis liquor (PHL) of kraft-based dissolving pulp production process. The results showed that the NSSC and PHL spent liquors had some lignin-carbohydrate complexes (LCC), and that the square weighted counts of particles with a chord length of 50-150μm in the spent liquors were significantly increased as pH dropped to 1.5. Interestingly, the acidification reduced the lignosulfonate/lignin content of NSSC and PHL by 13% or 20%, while dropped their oligosugars content by 75% and 38%, respectively. On a dry basis, the precipitates had more carbon, hydrogen and a high heating value of 18-22MJ/kg, but less oxygen, than spent liquors. The precipitates of PHL could be used as fuel.

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 score0.007
Threshold uncertainty score0.388

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 head0.192
Teacher spread0.189 · 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

Citations48
Published2016
Admission routes2
Has abstractyes

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