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Record W3005959819 · doi:10.1021/acs.iecr.9b06116

Hydrodynamic Characteristics in a Cold Flow Model of Quadruple Fluidized Bed Gasifier

2020· article· en· W3005959819 on OpenAlexaff
Jie Ren, Ziliang Wang, Boshu He, Linbo Yan, Ziqi Wang, Guangchao Ding, Zhaoping Ying

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2020
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsWood gas generatorFluidized bedBubbleFlow (mathematics)MechanicsChemical looping combustionFluidized bed combustionMaterials scienceEnvironmental scienceNuclear engineeringProcess engineeringWaste managementEngineeringPhysicsCoal

Abstract

fetched live from OpenAlex

Quadruple fluidized bed gasifier (QFBG) is a new kind of clean power-generation system, which consists of a dual fluidized bed (DFB) gasifier and a dual fluidized bed of chemical looping. With high gasification efficiency and low carbon emissions, QFBG has attracted extensive attention. Hydrodynamic characteristics of the quadruple fluidized bed (QFB) system are the base of modeling and designing of a hot reactor. To fully understand the hydrodynamic characteristics, a cold flow model of the QFB system on a laboratory scale was designed and set up. In the cold model, operation parameters such as bubble fluidized bed velocity, riser velocity, and total solid inventory (TSI) were investigated. Meanwhile, an exclusive pressure balance equation of QFB is proposed. The QFB was also compared with DFB. The results from the experiment show that the QFB system has a positive response to the operation parameters and better solid transport capacity than the typical DFB system.

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 score0.034
Threshold uncertainty score0.068

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.111
GPT teacher head0.291
Teacher spread0.180 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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