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Record W1502226219 · doi:10.15376/biores.3.2.363-382

Liquefaction of corn distillers dried grains with solubles (DDGS) in hot-compressed phenol

2008· article· en· W1502226219 on OpenAlexafffund
Chunbao Xu, Huasu Sua, Daqiang Cang

Bibliographic record

VenueBioResources · 2008
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaLakehead University
KeywordsLiquefactionPhenolYield (engineering)CharChemistryCokeCatalysisMaterials scienceOrganic chemistryChemical engineeringPyrolysisMetallurgy

Abstract

fetched live from OpenAlex

The present work aims to liquefy corn Distillers Dried Grains with Solubles (DDGS), a by-product in corn ethanol production, to produce liquid products as a potential substitute for petroleum-derived phenol or an additive in the manufacturing of phenolic resins. Efficient liquefaction of DDGS has been realized with hot-compressed phenol at 200-450°C. Effects of liquefaction temperature, initial phenol/DDGS ratio (wt/wt), residence time, and catalysts on yields of liquefaction products, as well as characterizations of typical liquid and solid products, have been investigated. The yield of liquid products increased with temperature, while it peaked at around 300°C, due to condensation/cracking reactions of the liquid intermediates/products to form solid products (i.e., coke/char) at temperatures higher than 300°C. The liquid yield increased monotonically with the initial phenol/DDGS ratio for a specified treatment time and temperature. An increased residence time generally produced a lower yield of liquid products, and a higher yield of solid residue, which was likely due to the increased coke/char formation from liquid products for a prolonged residence time. The yield of liquid products reached a maximum of 97 wt% at 300°C for 5 min-operation with Phenol/DDGS of 2/1. K2CO3, Rb2CO3, and NaOH were found very effective catalysts for enhancing the liquid yields. The use of these catalysts resulted in a 25% relative increase in the liquid yields for 5 minute operation with Phenol/DDGS of 1/1 at 300°C.

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 score0.108
Threshold uncertainty score0.432

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 head0.010
GPT teacher head0.174
Teacher spread0.164 · 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

Citations12
Published2008
Admission routes2
Has abstractyes

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