MétaCan
Menu
Back to cohort
Record W1973585343 · doi:10.1021/ef000024r

Agglomeration and Fouling in Three Industrial Petroleum Coke-Fired CFBC Boilers Due to Carbonation and Sulfation

2000· article· en· W1973585343 on OpenAlexaff
Edward J. Anthony, R. E. Talbot, Lei Jia, D. L. Granatstein

Bibliographic record

VenueEnergy & Fuels · 2000
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsFoulingPetroleum cokeCarbonationEconomies of agglomerationCokeFluidized bed combustionWaste managementSorbentEnvironmental scienceSuperheaterSulfur dioxideBoiler (water heating)ChemistryFluidized bedChemical engineeringEngineeringAdsorption

Abstract

fetched live from OpenAlex

Petroleum coke is quickly becoming the fuel of choice for many FBC boiler operators, due to its low cost, high availability and high heating value. However, these inherent benefits come with a price, as the high sulfur content of coke requires limestone use as a sorbent for sulfur capture. In some cases, operational problems associated with limestone use have arisen. Fouling, in terms of solid deposits in such boilers are normally thought to occur as a result of interaction with various fuel-ash-derived species within the system. However, detailed examination of the solid deposits demonstrated that the fouling was, most generally, associated with an agglomeration mechanism we have called extended sulfation, i.e., sulfation to near quantitative levels of the limestone sorbent. Carbonation and hydration have also been found to play a role in the agglomeration process at lower temperatures. This paper describes the fouling mechanisms in three circulating fluidized bed boilers firing petroleum coke as the only fuel.

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 score0.210
Threshold uncertainty score0.501

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 head0.011
GPT teacher head0.191
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 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

Citations37
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207