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Record W4401480623 · doi:10.31857/s2686740024010128

Increasing the efficiency of the use of the energy of a steam explosion in the processing of lignin-cellulose raw materials

2024· article· en· W4401480623 on OpenAlexaboutno aff
I. N. Grishnyaev

Bibliographic record

VenueДоклады Российской академии наук Физика технические науки · 2024
Typearticle
Languageen
FieldEngineering
TopicCoal Combustion and Slurry Processing
Canadian institutionsnot available
Fundersnot available
KeywordsSteam explosionRaw materialNozzleLigninCelluloseMaterials scienceWaste managementProcess engineeringEnvironmental sciencePulp and paper industryNuclear engineeringChemical engineeringChemistryMechanical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

An analysis was made of the physical processes occurring during a steam explosion and the exit from the reactor of a stream containing lignocellulosic raw materials and a vapor-liquid medium. It is shown that it is necessary to install a Laval nozzle at the reactor outlet. By selecting the design parameters of the reactor and the Laval nozzle, it is possible to ensure the occurrence of a self-oscillating mode of the vapor-liquid medium in the expanding part of the nozzle. Lignocellulosic raw materials passing through the emerging shocks is subjected to additional impact, which leads to its destruction, an increase in the specific surface area, and, consequently, to an increase in reactivity.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.224
Teacher spread0.195 · 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 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
Published2024
Admission routes1
Has abstractyes

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Same venueДоклады Российской академии наук Физика технические наукиSame topicCoal Combustion and Slurry ProcessingFrench-language works237,207