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Record W2056837601 · doi:10.1002/bbb.1556

Is the pipeline hydro‐transport of wheat straw and corn stover to a biorefinery realistic?

2015· article· en· W2056837601 on OpenAlexafffund
Mahdi Vaezi, Balwinder Nimana, Amit Kumar

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2015
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Alberta
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaAlberta Innovates Bio SolutionsNational Science Council
KeywordsCorn stoverBiorefineryEnvironmental scienceStrawSlurryBiofuelBiomass (ecology)BioenergyAgriculturePulp and paper industryEnvironmental engineeringAgronomyWaste managementEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract Pipeline hydro‐transport is an alternative to truck delivery of agricultural residue (lignocellulosic) biomass. Pipeline hydro‐transport benefits from economies of scale, reduces total delivery costs, and enables bio‐based energy facilities to achieve higher capacities. In this study, the empirical correlation based on experimentally developed data for pipeline transport of agricultural residue‐water mixtures (slurry) was used to develop a data‐intensive techno‐economic model to estimate the cost of pipeline hydro‐transport of wheat straw and corn stover to a bioethanol refinery. The total cost of pipeline hydro‐transport was found to be lowest at 8.8% dry matter slurry solid mass content and 2.5 m s −1 slurry velocity. At this biomass slurry solid mass content and velocity, the pipeline hydro‐transport of biomass was found to be economically more viable than truck delivery at capacities of 0.45 M dry t yr −1 or more for a one‐way pipeline and 1.4 M dry t yr −1 or more for a two‐way pipeline (with the return of the carrier liquid). The ability to economically hydro‐transport agricultural residue biomass in pipes offers the opportunity to develop large‐scale bioethanol plants. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

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 score0.313
Threshold uncertainty score0.720

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 head0.032
GPT teacher head0.231
Teacher spread0.199 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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