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Record W4313430959 · doi:10.3390/su142416908

Application and Optimization of the Parameters of the High-Level Boreholes in Lateral High Drainage Roadway

2022· article· en· W4313430959 on OpenAlexaff
Haiqing Shuang, Weitao Meng, Yulong Zhai, Peng Xiao, Yu Shi, Yu Tian

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsPolytechnique Montréal
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsBoreholeCoal miningGeologyCoalRoofVolume (thermodynamics)Petroleum engineeringMining engineeringGeotechnical engineeringEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The key parameters of high-level boreholes in high drainage roadways affect the gas treatment effect of the working face directly. Therefore, the layout parameters of high-level boreholes in the lateral high drainage roadway (LHDR) are determined and optimized as necessary. Based on the LHDR layout on the 2-603 working face of the Liyazhuang coal mine, the key technological requirements on high-level borehole parameters were analyzed and the distribution characteristics of the gas volume fraction in the coal roof were studied. The gas migration law in the mined-out areas was obtained and the layout locations of high-level boreholes were determined finally. The research demonstrates that the high-level boreholes lag the 2-603 working face distance and the position of the final borehole (the position of the final borehole in this paper refers to the distance between the final borehole and the roof) influence the stability of boreholes and the gas extraction effect. The distribution of the gas volume fraction from the intake airway to the return airway can be divided into the stable stage, slow growth stage, and fast growth stage. Influenced by the flow field in the mined-out areas, the mean volume fraction of the borehole-extracted gas has no obvious relationship with the gas volume fraction at the upper corner. According to the final optimization, the high-level borehole is determined as having a 15 m lag behind the working face and the position of the final borehole is 44 m away from the coal seam roof. These have been applied successfully in engineering practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes1
Has abstractyes

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