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Record W2022347660 · doi:10.1021/ie1003625

Kinetic Modeling of Hemicellulose Hydrolysis from Triticale Straw in a Pressurized Low Polarity Water Flow-Through Reactor

2010· article· en· W2022347660 on OpenAlexafffund
C. Pronyk, Giuseppe Mazza

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2010
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsAgriculture and Agri-Food Canada
FundersScheme for Promotion of Academic and Research CollaborationAgriculture and Agri-Food CanadaAuburn University
KeywordsHemicelluloseChemistryArrhenius equationContinuous stirred-tank reactorArrhenius plotHydrolysisReaction rate constantVolumetric flow rateKinetic energyYield (engineering)ThermodynamicsKineticsOrganic chemistryActivation energyPhysical chemistry

Abstract

fetched live from OpenAlex

Two classic and one modified kinetic models were used to study the hydrolysis of triticale straw using pressurized low-polarity water (PLPW) in a flow-through reactor. Results indicated that for these experiments hemicellulose did not follow a biphasic reaction pathway. High yields of 72% were achieved at 170 °C, but these decreased to 60% at 150 °C and only 13% at 130 °C. The kinetics were controlled more by increases in temperature than flow rate in the reactor. Increases in flow rate reduced the overall hemicellulose yields but increased the portion extracted as oligomers. The kinetic rate constants when plotted as an Arrhenius-type temperature relationship displayed a dependency with flow rate. Curvature in the Arrhenius plots of the kinetic rate constants was due to differences in acetic acid formation with temperature. A modified monophasic kinetic model which incorporated reactor geometry and fluid flow was successful at modeling the yield of xylo-oligomers and monomers in PLPW.

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)0.000
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.047
GPT teacher head0.269
Teacher spread0.222 · 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

Citations58
Published2010
Admission routes2
Has abstractyes

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