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Effect of Steam Explosion Pretreatment on Size Reduction and Pellet Quality of Woody and Agricultural Biomass

2015· book-chapter· en· W2505759104 on OpenAlexaff
Pak Sui Lam, Pak Yiu Lam, Shahab Sokhansanj, Xiaotao Bi, C. Jim Lim, Staffan Melin

Bibliographic record

VenueIGI Global eBooks · 2015
Typebook-chapter
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSteam explosionPelletsPelletCellulosic ethanolWaste managementBiomass (ecology)Pulp and paper industryRaw materialEnvironmental scienceMoistureSawdustMaterials scienceComposite materialAgronomyEngineeringChemistryCelluloseChemical engineering

Abstract

fetched live from OpenAlex

Steam explosion is a thermo-chemical pretreatment widely used to disrupt the ultra-structure of the cell wall of the ligno-cellulosic fiber to improve the fractionation of the major ligno-cellulosic components of the biomass for biochemical conversion. In recent years, steam explosion pretreatment has been applied on the fibers for improving the pellet quality of woody and agricultural biomass for thermo-chemical conversion. The improved qualities include high bulk density, low equilibrium moisture content, higher heating value, mechanical strength and moisture resistance. All of these desirable properties allow the steam exploded pellets to be handled and stored outdoors safely, similar to coal. This also raises lots of interests in considering pellets as preferable feedstock for the thermal power plant or bio-refinery facilities. In this chapter, the state of art of research findings on the effect of steam explosion on size reduction and pellet quality of woody and agriculture biomass will be discussed.

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 score0.303
Threshold uncertainty score0.805

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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