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Record W2039523597 · doi:10.1115/power2014-32207

Improve Boiler Reliability With Unit Specific Strategic Planning

2014· article· en· W2039523597 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsnot available
Fundersnot available
KeywordsRisk analysis (engineering)Preventive maintenanceStrategic planningReliability engineeringOperations managementComputer scienceEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

Boiler tube failures remain the leading cause of lost availability in power boilers across global markets. The need for strategic planning in regard to inspections, preventative maintenance and targeted replacements has never been greater. Identifying the root problem(s) is essential and must be properly managed for continued safety, reliability and availability. The process associated with integrating a boiler management program can be viewed as an insurmountable obstacle for many utility operators and owners. In many cases, the cookie cutter approach that is often used results in insufficient reliability recovery. However, using modern technology and tactics to strategically manage and properly identify specific operating and design conditions has proven exceedingly successful in reducing a unit’s forced outage rate [EFOR]. Specific challenges plants are faced with include the reduction of onsite engineers, aging workforces and equipment, and the need to remain competitive in a challenging global energy market. Plant managers are routinely faced with the complex task of determining the current condition of their equipment, forecasting outage budgets and schedules, and performing risk assessments. Additionally, insurance companies are increasingly requiring inspection and maintenance records that are not always up-to-date or readily available. The solutions to reducing the EFOR of a unit involves taking a comprehensive approach to boiler management utilizing unit specific operational training, advanced data management, and strategic inspection, maintenance and replacement prioritization. Implementing this comprehensive approach has awarded millions in savings for plant managers that have adopted this strategy. Implementing a unit specific, target driven, and strategic plan enables utility owners and operators to succeed in today’s competitive market by increasing the unit’s reliability and availability without sacrificing safety or environmental standards. Thielsch Engineering, Inc. developed a program titled: 4-SYTE System Strategy that is currently utilized in more than 60 power plants within the United States and Canada. Unit specific strategic planning is necessary for all facilities that rely on these critical components. Advanced technology must be adopted by all energy producers to ensure they remain competitive and profitable.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.548

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 head0.196
Teacher spread0.185 · 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

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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