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Record W4294151235 · doi:10.1002/cjce.24633

Biorefineries in Kraft pulp mills for biofuels production: A critical review

2022· review· en· W4294151235 on OpenAlexvenueno aff
Kátia D. Oliveira, Pedro Henrique González de Cademartori, Graciela Inês Bolzón de Muñiz, Luiz F. L. Luz‐Junior, C.N. Ávila-Neto

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typereview
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBlack liquorBiofuelWaste managementKraft paperKraft processBiorefineryPulp and paper industryEnvironmental sciencePulp millCombustionBoiler (water heating)Biomass (ecology)PyrolysisBioproductsPulp (tooth)EngineeringChemistryLigninOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Kraft pulp production generates residues and by‐products of significant importance to the mill. Solid residues from forestry activities are commonly used to generate steam in power boilers. In the recovery cycle, black liquor generates steam (and subsequently energy) by burning in the Tomlinson boiler, while white liquor is regenerated. Well‐developed alternative technologies can use these residues and by‐products to generate different types of biofuels. This review addresses the use of such technologies integrated with Kraft mills, in the concept of biorefineries, showing advantages, disadvantages, and successful examples. Solid residues from forestry can be used to produce bio‐oil through processes such as fast pyrolysis and hydrothermal liquefaction. Bio‐oils are currently used for heating through combustion in commercial/industrial boilers, but greater appreciation occurs if used as biofuels, which is done through catalytic upgrading processes. Black liquor gasification generates synthesis gas, which can be burned for energy co‐generation, used to produce synthetic fuels, or as a hydrogenating agent for bio‐oil or crude tall oil catalytic upgrading. Kraft biorefineries are gradually being implemented, justifying efforts to improve existing and new biomass conversion technologies.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.258
Teacher spread0.223 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2022
Admission routes1
Has abstractyes

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