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Record W1983393935 · doi:10.1115/1.2134734

Assessment of Sorbent Reactivation by Water Hydration for Fluidized Bed Combustion Application

2005· article· en· W1983393935 on OpenAlex
Fabio Montagnaro, Piero Salatino, Fabrizio Scala, Yinghai Wu, Edward J. Anthony, Lufei Jia

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Energy Resources Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSorbentFluidized bed combustionWaste managementCombustionBoiler (water heating)Fluidized bedEnvironmental scienceRaw materialMaterials scienceChemical engineeringPulp and paper industryChemistryAdsorptionEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Disposal of fluidized bed combustion (FBC) solid residues currently represents one of the major issues in FBC design and operation, and contributes significantly to its operating cost. This issue has triggered research activities on the enhancement of sorbent utilization for in situ sulfur removal. The present study addresses the effectiveness of the reactivation by liquid water hydration of FB spent sorbents. Two materials are considered in the study, namely the bottom ash from the operation of a full-scale utility FB boiler and the raw commercial limestone used in the same boiler. Hydration-reactivation tests were carried out at temperatures of 40°C and 80°C and for curing times ranging from 15minutes to 2d, depending on the sample. The influence of hydration conditions on the enhancement of sulfur utilization has been assessed. A combination of methods has been used to characterize the properties of liquid water-hydrated materials.

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.

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 score0.050
Threshold uncertainty score0.314

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 head0.003
GPT teacher head0.212
Teacher spread0.208 · 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