Hybrid method for analyzing air thermal conditions in underground mines

Ihsan, Ahmad; Cheng, Jianwei; Widodo, Nuhindro Priagung; Wang, En-yuan; Waly, Fadli Zaka; Syachran, Satria Rum; Fadillah, Taruna; Khamidah, Halumi Nur

doi:10.1016/j.eswa.2023.123026

Cited by 1 publication

(1 citation statement)

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“…AI models, such as the PSO-SVR proposed by Deng et al ( 2018 ), can predict coal spontaneous combustion temperatures based on gas concentrations in working areas. Ihsan et al ( 2024a ) applied a hybrid integrated numerical method and an ANFIS method to predict the wet bulb globe temperature.…”

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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Section: Application Areas Of Ai Technologiesmentioning

confidence: 99%