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Record W2155437809 · doi:10.5539/cis.v7n4p30

Research on the Optimization of Boiler Efficiency based on Artificial Bee Colony Algorithm

2014· article· en· W2155437809 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2014
Typearticle
Languageen
FieldComputer Science
TopicMetaheuristic Optimization Algorithms Research
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Guangdong Province
KeywordsBoiler (water heating)Computer scienceProcess engineeringCombustionAlgorithmWaste managementEngineeringChemistry

Abstract

fetched live from OpenAlex

This paper is to seek effective scheme of boiler efficiency optimization, it uses Artificial Bee Colony (ABC) algorithm to optimize boiler efficiency based on the model of boiler combustion efficiency. First, an optimization model of boiler efficiency, which takes boiler efficiency function as optimization objective, is set up according to the heat loss of boiler combustion. Moreover, the operating parameters affecting boiler efficiency is determined. Then ABC algorithm is used to solve the optimal value of boiler efficiency. The result of the research shows that the optimization of boiler efficiency based on ABC algorithm can quickly obtain optimal parameters for running boiler, so that the optimal boiler efficiency can be got.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.457
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.043
GPT teacher head0.325
Teacher spread0.281 · 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