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Record W4406859613 · doi:10.1515/npprj-2024-0073

Energy consumption in refiner mechanical pulping

2025· article· en· W4406859613 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical and Thermal Properties Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPulp and paper industryEnergy consumptionEnvironmental scienceProcess engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract The efficiency of mechanical pulping has long been of interest due to the large energy consumed by the process. Previous estimates of theoretical efficiency have accounted for less than 20 % of the energy employed. In this study, we make new estimates based on fracture mechanics and abrasion as the mechanisms of new surface creation. We postulate that fracture mechanics comminutes wood into fibres and creates pores in fibre walls. This consumes around 100 kWh/t. Abrasion peels surface material from fibres in the form of morphologically different fines particles. Based on abrasion theory, we estimate this specific energy to be around 1,330 kWh/t. Together, fracture mechanics and abrasion, account for about 70 % of the specific energy (2,000 kWh/t) to produce TMP for printing paper grades. We postulate that the remaining energy is consumed as hysteresis losses from viscoelastic strains not linked to creation of new surface. The largest single source of energy consumption, abrasion, alone accounts for about 66 % of the energy of the process. Finally, we discuss how energy may be reduced by refining intensity and other means.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.320
Teacher spread0.270 · 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