ESTIMATION OF COAL SEAM METHANE CONTENTS USING FUZZY LOGIC METHOD

Year 2022, Volume: 30 Issue: 3, 471 - 480, 21.12.2022

Nilüfer Kurşunoğlu

https://doi.org/10.31796/ogummf.1135126

Cited By: 1

Abstract

The coalification process results in variations in both the physical properties and chemical structure of the coal. Sorption capacity is a characteristic feature of coal. Methane is one of the major threats in underground coal mines and also an environmental issue of gas emissions from coal mining. Methane content depends on a range of processes such as gas migration, accumulation, and generation. Its quality and quantity differ substantially depending on the rank and depth of the coal seam. With the increase in mining depths, methane becomes an important danger due to the risk of explosion. Therefore, the prediction of methane contents according to different and changing working conditions is an important issue in terms of mine safety. The Fuzzy Logic method which provides a quick and reliable solution was preferred to estimate coal seam methane contents in this study. The aim of the study is to propose an alternative way to prevent possible mining accidents by estimating coal seam methane contents using the Fuzzy Logic methodology. The model results were compared with the field methane contents. The results indicate that the Fuzzy Logic model can provide a reliable prediction way with a 91% success rate.

Keywords

Coal, mine, methane, fuzzy logic, safety

KÖMÜR DAMARI METAN İÇERİKLERİNİN BULANIK MANTIK YÖNTEMİ İLE TAHMİNİ

Abstract

Kömürleştirme işlemi, kömürün hem fiziksel özelliklerinde hem de kimyasal yapısında değişikliklere neden olur. Sorpsiyon kapasitesi, kömürün karakteristik bir özelliğidir. Metan, yeraltı kömür madenlerinde önemli tehtidlerden biridir ve ayrıca kömür madenciliğinden kaynaklanan gaz emisyonlarının çevresel bir sorunudur. Metan içeriği, gaz göçü, birikimi ve üretimi gibi bir dizi sürece bağlıdır. Kalitesi ve miktarı, kömür damarının derecesine ve derinliğine bağlı olarak büyük ölçüde farklılık gösterir. Üretim derinliklerinin artmasıyla birlikte metan, patlama riski nedeniyle önemli bir tehlike haline gelmektedir. Bu nedenle metan içeriklerinin farklı ve değişen çalışma koşullarına göre tahmin edilmesi maden güvenliği açısından önemli bir konudur. Bu çalışmada kömür damarı metan içeriklerinin tahmininde hızlı ve güvenilir bir çözüm sunan Fuzzy Logic yöntemi tercih edilmiştir. Çalışmanın amacı, Bulanık Mantık yöntemi ile kömür damarı metan içeriklerini tahmin ederek olası maden kazalarını önlemek için alternatif bir yol önermektir. Model sonuçları yerinde metan içerikleri ile karşılaştırılmıştır. Sonuçlar, Bulanık Mantık modelinin %91 başarı oranı ile güvenilir bir tahmin aracı olabileceğini göstermektedir.

Keywords

Kömür, maden, metan, bulanık mantık, güvenlik

References

• Chatterjee, R., & Paul, S. (2013). Classification of coal seams for coal bed methane exploitation in central part of Jharia coalfield, India – A statistical approach. Fuel. 111, 20-29. doi: https://doi.org/10.1016/j.fuel.2013.04.007
• Fisne, A., & Esen, O. (2014). Coal and gas outburst hazard in Zonguldak coal basin of Turkey, and association with geological parameters. Natural Hazards. 74, 1363-1390. doi: https://doi.org/10.1007/s11069-014-1246-9
• Gao, L., Mastalerz, M., & Schimmelmann, A. (2014). The Origin of coalbed methane. Elsevier, USA.
• He, H., Zhao, Y., Zhang, Z., Gao, Y., & Yang, L. (2016). Prediction of coalbed methane content based on uncertainty clustering method. Energy Exploration & Exploitation. 34, 273-281. doi: https://doi.org/10.1177/0144598716630163
• Hemza, P., Sivek, M., & Jirásek, J. (2009). Factors influencing the methane content of coal beds of the czech part of the Upper Silesian coal basin, Czech Republic. International Journal of Coal Geology. 79, 29-39. doi:https://doi.org/10.1016/j.coal.2009.04.003
• Hu, X., Yang, S., Zhou, X., Zhang, G., & Xie, B. (2014). A quantification prediction model of coalbed methane content and its application in Pannan coalfield, Southwest China. Journal of Natural Gas Science and Engineering. 21, 900-906. doi: https://doi.org/10.1016/j.jngse.2014.10.017
• Islam, M. R., & Hayashi, D. (2008). Geology and coal bed methane resource potential of the Gondwana Barapukuria coal basin, Dinajpur, Bangladesh. International Journal of Coal Geology. 75, 127-143. doi:https://doi.org/10.1016/j.coal.2008.05.008
• Jianqing, Z. (2011). Study on the gas content of coal seam based on the BP Neural Network. Procedia Engineering. 26, 1554-1562. doi:https://doi.org/10.1016/j.proeng.2011.11.2338
• Karacan, C. Ö., Ruiz, F. A., Cotè, M., & Phipps, S. (2011). Coal mine methane: A review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction. International Journal of Coal Geology. 86, 121-156. doi:https://doi.org/10.1016/j.coal.2011.02.009
• Kędzior, S. (2009). Accumulation of coal-bed methane in the south-west part of the Upper Silesian coal basin (southern Poland). International Journal of Coal Geology. 80, 20-34. doi:https://doi.org/10.1016/j.coal.2009.08.003
• Kędzior, S. (2015). Methane contents and coal-rank variability in the Upper Silesian coal basin. Poland. International Journal of Coal Geology. 139, 152-164. doi:https://doi.org/10.1016/j.coal.2014.09.009
• Kędzior, S., & Dreger, M. (2019). Methane occurrence, emissions and hazards in the Upper Silesian coal basin, Poland. International Journal of Coal Geology. 211, 103226.doi:https://doi.org/10.1016/j.coal.2019.103226
• Kursunoglu, N., & Onder, M. (2019). Application of structural equation modeling to evaluate coal and gas outbursts. Tunnelling and Underground Space Technology. 88, 63-72. doi:https://doi.org/10.1016/j.tust.2019.02.017.
• McPherson, M.J. (1993). Subsurface Ventilation Environmental and Engineering. Chapman & Hall.
• Paul, S., Ali, M., & Chatterjee, R. (2021). Prediction of velocity, gas content from neural network modeling and estimation of coal bed permeability from image log in coal bed methane reservoirs: Case study of South Karanpura Coalfield, India. Results in Geophysical Sciences. 7, 100021.doi: https://doi.org/10.1016/j.ringps.2021.100021
• Prasad, C. V. K. (2012). Determination of gas content of coal. Natıonal Instıtute of Technology. Rourkela.
• Ross, T. J. (2017). Fuzzy logic with engineering applications. Fourth Edition. John Wiley & Sons. UK.
• Saghafi, A., Williams, D. J., & Battino, S. (1998). Accuracy of measurement of gas content of coal using rapid crushing techniques. Coal Operators' Conference. Wollongong.
• Shatnawi, M., Shatnawi, A., AlShara, Z., & Husari, G. (2021). Symptoms-based fuzzy-logic approach for covıd-19 diagnosis. International Journal of Advanced Computer Science and Applications. 12, 444-452. doi:https://doi.org/10.14569/IJACSA.2021.0120457
• Thakur, P. (2011). Gas and Dust Control. SME Mining Engineering Handbook, 3th Edition, Published by SME Inc. USA.
• THE, (2020). Turkish Hardcoal Enterprise. Accessed address:http://www.taskomuru.gov.tr/file/2020_faaliyet.pdf.
• Yen, J., Langari, R., 1999. Fuzzy logic intelligence, control, and information, Prentice Hall, New Jersey.
• YuMin, L., DaZhen, T., Hao, X., & Shu, T. (2011). Productivity matching and quantitative prediction of coalbed methane wells based on BP neural network. Science China Technological Sciences. 54, 1281–1286.doi: https://doi.org/10.1007/s11431-011-4348-6
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control. 8, 338-353. doi:https://doi.org/10.1016/S0019-9958(65)90241-X.
• Zawadzki, J., Fabijańczyk, P., & Badura, H. (2013). Estimation of methane content in coal mines using supplementary physical measurements and multivariable geostatistics. International Journal of Coal Geology. 118, 33-44. doi:https://doi.org/10.1016/j.coal.2013.08.005
• Zeng, B., & Li, H. (2021). Prediction of Coalbed Methane Production in China Based on an Optimized Grey System Model. Energy Fuels. 35, 4333−4344.doi: https://dx.doi.org/10.1021/acs.energyfuels.0c04195
• Zhu, H., Liu, P., Chen, P., & Kang, J. (2017). Analysis of coalbed methane occurrence in Shuicheng coalfield, southwestern China. Journal of Natural Gas Science and Engineering. 47, 140-153. doi:https://doi.org/10.1016/j.jngse.2017.09.003