Evaluation of time series artificial intelligence models for real-time/near-real-time methane prediction in coal mines

Demirkan, D. C.; Düzgün, Şebnem; Juganda, Aditya; Brune, Jürgen F.; Bogin, Gregory E.

doi:10.1080/19236026.2022.2072664

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Each presentation also proposed the implementation of novel technologies and safety protocols that will assist us in addressing our research questions RQ2 through RQ3. Doga et al [34] assessed the efficacy of artificial intelligence (AI) to anticipate real-time explosion hazards. Seven distinct data sets were employed to evaluate the predictive capabilities of ten time-varying algorithms.…”

Section: Prediction Of Methane Emission In Coal Mines -Literature Reviewmentioning

confidence: 99%

Management of methane emission in coal mines using artificial neural networks: A systematic review

Sundas Matloob,

Li Yang,

Sumaiya Bashiru Danwana

et al. 2024

Int. J. Sci. Res. Arch.

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Underground mines are responsible for a large number of the released methane worldwide. Our study paper can contribute to mitigating methane emissions, hence reducing the concentration about greenhouse emissions within the environment and mitigating the associated hazardous risks. In this study, more than 45 recent journals on Methane emission in Coal Mines were gathered from Web of Science, IEEE Xplore, ScienceDirect, and ResearchGate. A systematic review is accomplished of the past four years of various parts of Methane gas emission such as anthropogenic emission sources, gas emissions detection, prediction of methane using different technology and the Artificial intelligence projection model for methane emission. The outcomes reveal that since the methane emission management has obtained increasing attention over the past four years. This study also shows that big countries are using technology to control and utilize the methane emission to reduce the energy crisis. To decrease the coal mine injuries, academic understanding of underground methane management has increased, different technologies are integrated and support from various IT departments has amplified for the forecasting. In the future, the most critical task for coal mines risk assessment is to restore the worker's trust in mine safety, and the primary solution is to give more awareness to the underground management and workers through utilizing Artificial Intelligence (AI) mainly Artificial Neural Networks (ANN).

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Section: Prediction Of Methane Emission In Coal Mines -Literature Reviewmentioning

confidence: 99%

Management of methane emission in coal mines using artificial neural networks: A systematic review

Sundas Matloob,

Li Yang,

Sumaiya Bashiru Danwana

et al. 2024

Int. J. Sci. Res. Arch.

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“…When the excessive gas concentration was detected, timely alarms were issued to underground workers, enabling them to take appropriate measures to mitigate the risk of gas explosions. Through analysis and research on the system’s functionality, they proposed an overall construction approach and optimized the individual sub-systems to ensure the system’s comprehensive effectiveness [ 10 ]. Chen et al (2022) employed a Bayesian network as a research method and integrated expert knowledge and data learning to construct a Bayesian network risk recognition model for coal mine gas explosions.…”

Section: Literature Reviewmentioning

confidence: 99%

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

Li,

Zhang,

Yang

2023

PLoS ONE

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In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.

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Ventilation on demand in underground mines using neuro-fuzzy models: Modeling and laboratory-scale experimental validation

Ihsan,

Widodo,

Cheng

et al. 2024

Engineering Applications of Artificial Intelligence

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Evaluation of time series artificial intelligence models for real-time/near-real-time methane prediction in coal mines

Cited by 4 publications

References 23 publications

Management of methane emission in coal mines using artificial neural networks: A systematic review

Management of methane emission in coal mines using artificial neural networks: A systematic review

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

Ventilation on demand in underground mines using neuro-fuzzy models: Modeling and laboratory-scale experimental validation

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