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Record W3170488332 · doi:10.5539/eer.v11n1p86

Study on the Steam Source Selection of Feed Pump Turbine Based on Energy Matching

2021· article· en· W3170488332 on OpenAlexvenueno aff
Jiongming Wang, Feng Wang, Zhenglong Yu, Xiaochen Chen, Xiaojie Lin, Peng Zhang, Zhouyong Lin, Jincheng Chen, Hongyu Guan, Yutong Gong

Bibliographic record

VenueEnergy and Environment Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsBoiler feedwater pumpBoiler (water heating)Heat recovery steam generatorSteam turbineSteam-electric power stationSurface condenserProcess engineeringFeedwater heaterThermal power stationTurbineComputer scienceCombined cyclePower (physics)Superheated steamEnvironmental scienceMechanical engineeringEngineeringWaste managementThermodynamics

Abstract

fetched live from OpenAlex

The first phase of a power plant has two subcritical 300 MW units, and each boiler feed water pump is equipped with two with 50% capacity steam driven feed pumps and one 50% capacity electric start-up feed pump. The two steam pumps are put into operation during normal operation. After the heat supply transformation in the unit of the first phase of this power plant and based on the calculation and analysis of the heat balance of the unit, it is found that there will be insufficient output of the feed pump due to the insufficient steam source after the flow-through transformation, which will affect the water supply of the unit and reduce the energy utilization rate. To avoid this, the steam source of feed pump turbine needs to be selected again. Taking the original steam turbine condition of feed pump and the original steam source of feed pump turbine as the research basis, this paper makes analysis on the steam source transformation, and concludes the scheme of steam source selection and reconstruction based on energy matching. The feasibility of the scheme is tested and verified finally.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.023
GPT teacher head0.265
Teacher spread0.242 · 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.

Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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