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Record W2090585158 · doi:10.1002/ceat.201400503

Techno‐Economic Comparison of a 7‐MW<sub>th</sub> Biomass Chemical Looping Gasification Unit with Conventional Systems

2015· article· en· W2090585158 on OpenAlexaff
Milad Aghabararnejad, Gregory S. Patience, Jamal Chaouki

Bibliographic record

VenueChemical Engineering & Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsChemical looping combustionFluidized bedSyngasPressure swing adsorptionWood gas generatorWaste managementOxygenIntegrated gasification combined cycleAir separationBiomass (ecology)Process engineeringChemistryChemical engineeringEnvironmental scienceAdsorptionHydrogenEngineeringCoal

Abstract

fetched live from OpenAlex

Abstract The chemical looping process is an alternative method to provide conventional gasification (CG) systems with the required oxygen. The syngas produced via chemical looping has a higher calorific value than that generated by a conventional process with air. For comparison, a conventional gasification unit with pure oxygen (CGPO) and a chemical looping gasification (CLG) system were simulated with Aspen Plus. The CGPO reactor consisted of a bubbling fluidized bed and sand as bed material with oxygen supplied via a pressure swing adsorption unit. The CLG comprised a bubbling fluidized‐bed gasifier working in parallel with a fast fluidized‐bed oxidizer. The total capital investment (TCI) of the CLG unit was higher than that of the CGPO unit but the annual operating cost of the former was less which repays the difference in TCI in less than six years.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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

Citations34
Published2015
Admission routes1
Has abstractyes

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