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Record W2040041332 · doi:10.1115/fbc2005-78126

A Innovative Solution to the Problem of Mill Rejects in Thermal Power Plants

2005· article· en· W2040041332 on OpenAlexafffund
Animesh Dutta, Prabir Basu, Amit K. Ghosh, Prasun Chakraborty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityUniversity Canada WestUnited States Agency for International Development
KeywordsMillPulverized coal-fired boilerWaste managementBoiler (water heating)Power stationCoalThermal power stationEngineeringCombustionLeaching (pedology)Environmental scienceProcess engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The present paper discusses an innovative means for solving the problem of accumulation of waste coal in some pulverized coal fired plants. A major waste in a pulverized coal fired power plant is the reject produced from pulverizing mills. When coal is ground in a bowl mill heavy mineral matters are separated, but they still contain a small amount of coal mixed. However, the heating value of the rejects is generally too low for combustion in conventional boilers. As a result they find limited commercial use and are dumped in the plant adding to the fugitive dust, leaching problem and most importantly devastating damage to the landscape. The amount of mill reject is significant in older operating plant. In India for example, it is about 0.5–1.0% of coal throughput into the boiler. In this paper, an exercise is undertaken to show how these rejects could be burnt to produce low pressure process steam, which could save the bleeding of main steam of the power plant and thereby augment power generation. A subcompact novel circulating fluidized bed boiler has been designed, built and commissioned for this purpose. The present paper discusses the finding of above exercise.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.138

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations3
Published2005
Admission routes2
Has abstractyes

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