Real-Time Methane Prediction in Underground Longwall Coal Mining Using AI

Doga Cagdas Demirkan (), H. Sebnem Duzgun (), Aditya Juganda, Jurgen Brune and Gregory Bogin
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Doga Cagdas Demirkan: Mining and Earth Systems Engineering Program, Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
H. Sebnem Duzgun: Mining and Earth Systems Engineering Program, Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
Aditya Juganda: Mining and Earth Systems Engineering Program, Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
Jurgen Brune: Mining and Earth Systems Engineering Program, Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
Gregory Bogin: Mechanical Engineering Department, Colorado School of Mines, Golden, CO 80401, USA

Energies, 2022, vol. 15, issue 17, 1-12

Abstract: Detecting the formation of explosive methane–air mixtures in a longwall face is still a challenging task. Even though atmospheric monitoring systems and computational fluid dynamics modeling are utilized to inspect methane concentrations, they are not sufficient as a warning system in critical regions, such as near cutting drums, in real-time. The long short-term memory algorithm has been established to predict and manage explosive gas zones in longwall mining operations before explosions happen. This paper introduces a novel methodology with an artificial intelligence algorithm, namely, modified long short-term memory, to detect the formation of explosive methane–air mixtures in the longwall face and identify possible explosive gas accumulations prior to them becoming hazards. The algorithm was trained and tested based on CFD model outputs for six locations of the shearer for similar locations and operational conditions of the cutting machine. Results show that the algorithm can predict explosive gas zones in 3D with overall accuracies ranging from 87.9% to 92.4% for different settings; output predictions took two minutes after measurement data were fed into the algorithm. It was found that faster and more prominent coverage of accurate real-time explosive gas accumulation predictions are possible using the proposed algorithm compared to computational fluid dynamics and atmospheric monitoring systems.

Keywords: artificial intelligence (AI); computational fluid dynamics (CFD); underground coal mines; methane prediction; real-time; time series prediction; modified long short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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