An intelligent hybrid system for surface coal mine safety analysis

Lilić, Nikola; Obradović, Ivan; Cvjetić, Aleksandar

doi:10.1016/j.engappai.2010.01.025

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…Intelligent mining is a highly complex task [19][20][21], which involves integrating multiple disciplines and a variety of advanced technologies [22][23][24][25]. The parallel mining operating system can significantly enhance the safety of personnel and reduce the cost of operators, fuel, and maintenance.…”

Section: B Parallel Intelligent Miningmentioning

confidence: 99%

Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence

Chen

Wang

et al. 2021

2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI)

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With the rapid development and modernization requirement of global coal industry, there is an emerging need for intelligent and unmanned mining systems. In this paper, the Intelligent Mining Operating System (IMOS) is proposed and developed, based on the parallel management and control of mining operating infrastructure that integrates the intelligent mining theory, the ACP-based (Artificial societies, Computational experiments, Parallel execution) parallel intelligence approaches, and the new generation of artificial intelligence (AI) technologies. To satisfy the intelligent and unmanned demand of open-pit mines, the IMOS architecture is developed by integrating the theory of digital quadruplets. The main subsystems and functions of IMOS are elaborated in detail, including a single-vehicle operating subsystem, multi-vehicle collaboration subsystem, vehicle-road collaboration subsystem, unmanned intelligent subsystem, dispatch management subsystem, parallel management and control subsystem, supervisory subsystem, remote takeover subsystem, and communication subsystem. The IMOS presented in this paper is the first integrated solution for intelligent and unmanned mines in China, and has been implemented over ten main open pits in the past few years. Its deployment and utilization will effectively improve the production efficiency and safety level of open-pit mines, promote the construction of ecological mines, and bring great significance to the realization of sustainable mining development.

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Section: B Parallel Intelligent Miningmentioning

confidence: 99%

Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence

Chen

Wang

et al. 2021

2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI)

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“…Risk analysis is nevertheless one of the biggest concerns in the mining industry (e.g. Lilic et al, 2010). In order to improve risk management, an obvious first step is reliable identification of hazards, based on analysis of the available data (Komljenovic and Kecojevic, 2007;Tchankova, 2002).…”

Section: The Current Situationmentioning

confidence: 99%

“…Among the best-known categories, researchers have discussed occupational health and safety (OHS), financial, economic, operational, social, technical, organizational, legal, political and environmental risks (e.g. Bhattacharya et al, 2008;Daoud et al, 2011;Kumar, 2004;Lilic et al, 2010;Pal and Dewan, 2009;Shen, 2010). In addition to risks, practitioners and researchers must enumerate and integrate the various possible uncertainties (e.g.…”

Section: The Current Situationmentioning

confidence: 99%

A new practical approach to risk management for underground mining project in Quebec

Badri

Nadeau

Gbodossou

2013

Journal of Loss Prevention in the Process Industries

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“…With increasing attention being paid by state and relevant experts to environmental pollution caused by substantial coal mining, coal dust recognition technology based on imagery information is attracting more extensive and in-depth studies in mines locally and abroad because of its efficient visualization and intelligent application [1], [2], Technology to extract the visual information from imagery can solve safety issues in coal mines, provide an effective and highly reliable way to adapt to adverse environments, and accurately predict coal dust explosion hazards. However, the dust detection mechanism is extremely complex, and there are many determinants, resulting in significant randomness of the coal dust detection…”

Section: Introductionmentioning

confidence: 99%

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Wang

Zheng²,

Pan

et al. 2020

IEEE Access

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High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes ≥ 200 µm, 0.958 for [75 µm, 200 µm] and 0.950 for particle sizes < 75 µm. The proposed reduction approach offered a performance improvement in terms of more important attribute implementation. The paper demonstrated that the maximum information entropy reduction model can remove the redundant attributes without compromising the precision.

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An intelligent hybrid system for surface coal mine safety analysis

Cited by 15 publications

References 5 publications

Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence

Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence

A new practical approach to risk management for underground mining project in Quebec

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

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