Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network

Liu, Yujiao; Liu, Zeyi; Gao, Ke; Huang, Yuhan; Zhu, Chengyao

doi:10.3390/s22062096

Cited by 18 publications

(6 citation statements)

References 39 publications

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“…Through the joint implementation of sensors, artificial intelligence (AI), data analytics, Internet of Things (IoT), automation, augmented/virtual reality, robotization, and digital twins, it will be possible to simulate scenarios of mining production processes in real time, which will allow to analyze and study how different parameters or operating variables can modify or alter the performance of the mining operation, allowing this to go continuously from the real world to the meta-verse and return to the real world (physical–virtual–physical), making proactive decisions in favor of improving performance indicators [ 110 ].…”

Section: Discussionmentioning

confidence: 99%

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Cacciuttolo,

Guzmán,

Catriñir

et al. 2023

Sensors

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Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.

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Section: Discussionmentioning

confidence: 99%

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Cacciuttolo,

Guzmán,

Catriñir

et al. 2023

Sensors

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“…Liu et al ( 2022b ) studied optimal sensor placement for monitoring air distribution in mines, suggesting that equipping < 30% of tunnels with air velocity sensors ensures accurate air flow reconstruction if there are over 200 mine airways. Gao et al ( 2018 ) employed a modified genetic algorithm (GA) to solve the inverse air distribution problem, determining mine airway resistance from air flow and pressure measurements.…”

Section: Application Areas Of Ai Technologiesmentioning

confidence: 99%

“…In general, literature contains many works on the graphical methods for analyzing topologically complex mine ventilation networks (Maleki et al, 2018 ; Wu et al, 2019 ; Liu et al, 2022b ; Bosikov et al, 2023 ), but we do not discuss these works here, since they are not use AI technologies.…”

Section: Application Areas Of Ai Technologiesmentioning

confidence: 99%

Application of artificial intelligence in mine ventilation: a brief review

Semin,

Kormshchikov

2024

Front. Artif. Intell.

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In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.

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“…To solve the airflow reconstruction problem, Liu et al [44] proposed a new algorithm of an independent cut set depending on the underlying graph structure and evaluated its effectiveness in practical applications. The results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines).…”

Section: Trends In Intelligent Minesmentioning

confidence: 99%