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Record W2982707855 · doi:10.32964/tj12.2.29

Implications of converting a kraft pulp mill to a dissolving pulp operation with a hemicellulose extraction stage

2013· article· en· W2982707855 on OpenAlexaboutno aff
Enrique Mateos-Espejel, T. Radiotis, Naceur Jemaa

Bibliographic record

VenueTAPPI Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
Fundersnot available
KeywordsHemicelluloseDissolving pulpPulp and paper industryPulp (tooth)Kraft paperKraft processWaste managementPulp millCelluloseEngineeringEffluentChemical engineering

Abstract

fetched live from OpenAlex

Global demand for dissolving pulp has been increasing at a remarkable pace over the last few years. A shortage in cotton and the expansion of the textile, hygiene, and health product markets are behind this booming demand. The Canadian pulp and paper industry has entered these markets by converting several paper-grade pulp mills to dissolving pulp producers. In the kraft process, part of the hemicellulose remains with the pulp after cooking and the rest is burnt in the recovery boiler to produce energy. In dissolving pulp mills, most of the hemicellulose must be removed from the wood chips in a pre-hydrolysis stage before pulping. Hemicellulose hydrolysis and its subsequent extraction will affect energy and chemical balances. In addition, the new operation will require large capital expenditures. The objective of this work was to study the conversion of a kraft pulp mill to a dissolving pulp operation and the extraction of hemicelluloses from the process. The effects of hemicellulose extraction on mill energy balance, equipment requirements, and new operating conditions were analyzed. Computer simulations of the process and thermal pinch analysis were used. The existing bottlenecks (digesters, lime kiln, and recovery boiler) to increasing the dissolving pulp production capacity were identified before and after the conversion. In addition, energy efficiency measures were identified to decrease the energy consumption of the new 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations26
Published2013
Admission routes1
Has abstractyes

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