Research progress on intelligent coal caving theory and technology

The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-09-01
DOI
https://doi.org/10.13272/j.issn.1671-251x.18213
Citations
1
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research progress on intelligent coal caving theory and technology

1 citations
SHILAP Revista de lepidopterología
Rock Mechanics and Modeling
0.27
article

Research progress on intelligent coal caving theory and technology

article en
1 citations

Abstract

The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.

SHILAP Revista de lepidopterología
Openalex Percentile: Top 45%
Rock Mechanics and Modeling
0.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research progress on intelligent coal caving theory and technology · SHILAP Revista de lepidopterología (2024) | TGRS Research Map | TGRS