Refine your search
Collections
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Bilash Prajapati, Ram
- The prediction of caving sequence in bord and pillar workings using Random Forest algorithm
Abstract Views :77 |
PDF Views:0
Authors
Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, 826004, IN
2 National Institute of Rock Mechanics, Bangalore, Karnataka 560070, IN
3 Mahatma Gandhi Medical College and Hospital, Jamshedpur, Jharkhand, 831012, IN
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, 826004, IN
2 National Institute of Rock Mechanics, Bangalore, Karnataka 560070, IN
3 Mahatma Gandhi Medical College and Hospital, Jamshedpur, Jharkhand, 831012, IN
Source
Journal of Mines, Metals and Fuels, Vol 70, No 2 (2022), Pagination: 51-59Abstract
Depillaring of coal seams is of prime importance for coal mining industry in view of depleting superior quality coal reserve and increasing import of foreign coal. Depillaring in conjunction with caving is the most hazardous operation due to sudden roof fall. Some researchers have focused their work on roof fall risk assessment using statistical methods with a view to safety of men and machinery and to minimize accidents, down time and loss of production. Extensive research has not been done to predict roof caving sequence which is the basic requirement for successful caving operation for achieving production with zero harm potential. Roof caving is the result of interactions of all geotechnical and mining parameters including extraction area which is its main cause and contributory parameter. In this research, Random Forest, a supervised ensemble machine learning algorithm along with grid search and cross-validation is used to process the interactions among various parameters and to predict the sequential occurrence of roof caving and characterize the same as local or main fall with considerable and reliable accuracy.Keywords
Depillaring with caving, grid search, feature selection, local fall, machine learning, main fall, random forest, roof fall risk.
References
- Bradley A.E (1997): The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30(7), 1145–1159.
- Breiman L. (2001): Random Forests. Statistics Department University of California Berkeley, CA 94720, Jan,1–33.
- Chase F., Mark C and Heasley K. (2002): Deep cover pillar extraction in the U.S. Coalfields. In: Proceedings of the 21st International Conference on Ground Control in Mining. Morgantown, West Virginia University, USA; 2002, 69-80.
- Chen R.C., Dewi C., Huang S.W. and Caraka, R.E.(2020): Selecting critical features for data classification based on machine learning methods. J. of Big Data, 2020, 7:52,1– 26. https://doi.org/10.1186/s40537-020-00327-4
- DGMS(1993) Cir. (Tech.) Sapicom 3/1993 & 6/1993.
- Dixit M and Mishra K. (2010): A unique experience of on shortwall mining in Indian coal mining industry.In:Bose LK, Bhattacharya BC, Ghatak GP, Editors. Proceedings of the Third Asian Mining Congress. Kolkata : MGMI; 2010, 25–37.
- Duzgun H.S.B and Einstein H.H. (2004): Assessment and management of roof fall risks in underground coal mines.Safety Science (2004),42,23–41. https://doi.org/ 10.1016/S0925-7535(02)00067-X
- Ghasemi E., Ataei M., Shahriar K., Sereshki F and Esmaeil S. (2012): Assessment of roof fall risk during retreat mining in room and pillar coal mines. International Journal of Rock Mechanics and Mining Sciences, 54, 80–89. https:/ /doi.org/10.1016/j.ijrmms. 2012.05.025
- Goutte C and Gaussier E (2005): A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation. Lect Notes in Comput Sci, 2005, 3408 (April), 345–359.
- Gupta R.N and Prajapati R.B. (1997): Improvement of roof control by laterally confined fully grouted bolts. In: Proceedings of the 27th International Conference of Safety in Mines Research Institute, New Delhi., 2 (February, 1997), pp 729-741.
- Harshitha P., Dixith H.S., Krithi, Rajeeva N and Shashikala (2020): Coal Mine Disaster Prediction. Int. Journal of Engineering Research & Technology (IJERT), 2020, ISSN: 2278-0181, 9(07), 1447–1451.
- Jackins V., Vimal S., Kaliappan M and Lee, M. (2021): AI based smart prediction of clinical disease using random forest classifier and Naive Bayes. The Journal of Supercomputing, 2021, 77(5), 5198–5219. https://doi.org/ 10.1007/s11227-020-03481-x
- Maiti J. and Khanzode V.V. (2009): Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India. Safety Science, 47(8), 1068–1076. https://doi.org/10.1016/j.ssci. 2008.12.003
- Mandal P., Singh R., Singh A., Kumar R. and Sinha A (2006): State of art vis-a-vis Indian scenario of application of continuous miner based mass production technology. Journal of Mines Metals and Fuels, 2006, 54(12), 332–336.
- Mark C. (2010): Pillar design for deep cover retreat mining: ARMPS version 6 (2010). In: Proceedings of the 29th International Conference on Ground Control in Mining. Morgantown, West Virginia University, USA; 2010., 104–20.
- Mark C., Chase F and Pappas D (2003): Reducing the risk of ground falls during pillar recovery. Society of Mining, Metallurgy, and Exploration, Inc., Vol. 314 (January 2003), 153–160.
- Mohammadi S., Ataei M., Kakaie R., Mirzaghorbanali A., Rad Z. and Aziz, N. (2020): A roof cavability classification system and its use for estimation of main caving interval in longwall mining. Coal Operators’ Conference, Faculty of Engineering and Information Sciences, University of Wollongong, 2020, 104–115
- Motwani B (2020): Data Analytics using Python (First ed). Wiley India Pvt. Ltd.
- Sarantsatsral N., Ganguli R., Pothina R. and Tumen-Ayush B.A.(2021). A Case Study of Rock Type Prediction Using Random Forests:Erdenet Copper Mine, Mongolia. Minerals, 2021, 11 (1059), 1–12.
- Singh R., Mandal P.K., Singh A.K., Kumar R and Sinha A. (2011): Coal pillar extraction at deep cover/ : With special reference to Indian coalfields Bay of Bengal. Int. J. of Coal Geology, 2011, 86 (2–3), 276–288. https://doi.org/10.1016/ j.coal. 2011.03.003
- Singh R., Mandal P., Singh A., Kumar A and Sinha A. (2008): Optimal underground extraction of coal at shallow cover beneath surface/subsurface objects: Indian practices. Int J Rock Mech Rock Eng. 2008, 41(3), 421– 444.
- Singh R., Singh A.K., Maiti J., Mandal P. K., Singh R. and Kumar R.(2011): An observational approach for assessment of dynamic loading during underground coal pillar extraction. International Journal of Rock Mechanics and Mining Sciences, 48(5), 794–804. https:// doi.org/10.1016/j.ijrmms.2011.04.003
- Artificial Intelligence Model for Prediction of Local and Main FALL in caving Panel of Bord and Pillar Method of Mining
Abstract Views :93 |
PDF Views:0
Authors
Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand, IN
2 National Institute of Rock Mechanics, Bangalore – 560070, Karnataka, IN
3 All India Institute of Medical Sciences, Patna – 801507, Bihar, IN
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand, IN
2 National Institute of Rock Mechanics, Bangalore – 560070, Karnataka, IN
3 All India Institute of Medical Sciences, Patna – 801507, Bihar, IN
Source
Journal of Mines, Metals and Fuels, Vol 70, No 4 (2022), Pagination: 171-181Abstract
Depillaring with caving method of mining is a common practice in Indian coalfields and so is the occurrence of fall in goaf area, which can be considered as a boon in disguise as it allows wining of coal from large reserves but this becomes a curse just because of its unpredicted occurrence. Various empirical and statistical models are developed after idealization of several complicated mechanisms but they are not able to predict roof fall accurately especially in caving panels. Therefore, a new approach based on Artificial Intelligence is used to predict the sequence of local and main fall in caving panel taking into account a host of geotechnical and mining parameters of the mine. Mathematical equations and hidden calculations of artificial neural networks are known to have the capability of learning and analyzing records endlessly. Two different models have been deployed after optimal hyper parameter optimization to predict the occurrence of fall and to characterize the nature of fall (local or main) with considerable and reliable accuracy.Keywords
Bord and Pillar, Caving, Deep Learning Algorithm, Deep Neural Network, Hyper Parameter Optimization, Local Fall, Main Fall, TalosReferences
- Simon, H.A. (1957). Models of Man: Social and Rational. John Wiley and Sons, Inc., 1957.
- Malkowski, P., & Juszynski, D. (2021). Roof fall hazard assessment with the use of artificial neural network. International Journal of Rock Mechanics & Mining Sciences 143(2021): 104701. https://doi.org/10.1016/j.ijrmms.2021.104701
- Isleyen, E., Duzgun, S., & Carter, R. (2021). Interpretable deep learning for roof fall hazard detection in underground mines. Journal of Rock Mechanics and Geotechnical Engineering, 13(6): 1246–1255. https://doi.org/10.1016/j. jrmge.2021.09.005
- Razani, M.,Yazdani-chamzini, A., & Yakhchali, S. (2013). A novel fuzzy inference system for predicting roof fall rate in underground coal mines. Safety Science, 55: 26–33. https:// doi.org/10.1016/j.ssci.2012.11.008
- Deb, D., Kumar, A., & Rosha, R. (2006). Forecasting shield pressures at a longwall face using artificial neural networks. Geotechnical and Geological Engineering, 24: 1021–1037. https://doi.org/10.1007/s10706-005-4430-6
- Monjezi, M., Hesami, S., & Khandelwal, M. (2009). Superiority of neural networks for pillar stress prediction in bord and pillar method. Arabian Journal of Geosciences, 4: 845–853. https://doi.org/10.1007/s12517-009-0101-x
- Sheorey, P.R. (1984). Use of rock classification to estimate roof caving span in oblong workings. International Journal of Mining and Mineral Engineering, 2: 133–140. https:// doi.org/10.1007/BF00880878
- Singh, R., Singh, T.N., & Dhar, B.B. (1996). Coal pillar loading in shallow mining conditions. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 33, No.8: 757-768.
- Sheorey P.R. (1994). A Theory for in situ stresses in isotropic and transversely isotropic. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 31(1): 23–34. https://doi.org/10.1016/0148-9062(94)92312-4
- Jena, S., Prasad, K., Lokhande, R.D., & Pradhan, M. (2016). Analysis of strata control monitoring in underground coal mine for apprehension of strata movement. Recent Advances in Rock Engineering (RARE 2016), pp. 505–511. https://doi.org/10.2991/rare-16.2016.81
- Obert, L., & Duvall, W. (1967). Rock mechanics and the design of structures in rock. New York: John Wiley and Sons, Inc.; 1967.
- Majumdar, S. (1986). The support requirement at a longwall face — bending moment approach. In: Proceedings of 27th US Symposium on Rock Mechanics: Key to Energy Production (The University of Alabama, Tuscaloosa, Alabama); 1986: 325–332.
- Pawlowicz, K. (1967). Classification of rock cavability of coal measure strata in upper Silesia coalfield. Prace GIG, Komunikat, No. 429, Katowice. (in Polish).
- Peng, S.S., & Chiang, H.S. (1984). Longwall mining. In New York: John Wiley and Sons, Inc.
- Ghose, A.K., & Dutta, D. (1987). A rock mass classification model for caving roofs. International Journal of Mining and Geological Engineering, 5: 257–271. https://doi. org/10.1007/BF01560777
- Sarkar, S.K. (1998). Mechanized longwall mining — The Indian experiences. New Delhi: Oxford and IBH Publishing Company Private Limited.
- Sarkar, S.K., & Dhar, B.B. (1993). Strata control failures at caved longwall faces in India — experience from Rana to Churcha (1964 to 1990). In Proceedings of the 4th Asian Mining (Organized by MGMI at Calcutta), 361–380.
- Nimaje, D.S., & Sai, S. (2015). Development of software to evaluate roof fall risk in bord and pillar method — Depillaring Phase. GeoScience Engineering, LXI(2): 14–22. https://doi.org/10.1515/gse-2015-0014
- McCulloch, W., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. The Bulletin of Math. Biophys., 5: 115–133. https://doi.org/10.1007/BF02478259
- Fausett, L.V. (1993). Fundamentals of Neural Networks:Architectures, Algorithms and Applications. (1st ed). Pearson publication, India.
- Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational capabilities. In Proceedings of National Academy of Sciences, (USA), 79: 2554–2558. https://doi.org/10.1073/pnas.79.8.2554. PMid:6953413. PMCid:PMC346238
- Hopfield, J.J. (1984). Neurons with graded responses have collective computational properties like those of twostate neurons. In Proceedings of National Academy of Sciences (USA), 81: 3088–3092. https://doi.org/10.1073/ pnas.81.10.3088. PMid:6587342. PMCid:PMC345226
- Lee, S., Ryu, J., Lee, M., & Won, J. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 44(7): 820–833. https://doi.org/10.1007/s00254-003-0825-y
- Russell, S.J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (Third ed). Pearson India Education Services Pvt. Ltd.
- Haykin, S. (1999). Neural Networks:A Comprehensive Foundation. (2nd Ed.).
- Nazzal, J., El-Emary, I., & Najim, S. (2008). Multilayer Perceptron Neural Network (MLPs) for analyzing the properties of Jordan Oil Shale. World Applied Sciences Journal, 5(5): 546–552.
- Chollet, F., & Others. (2015). Keras. GitHub. 28. Van Rossum, G., & Drake, F.L. (2009). Python 3 Reference Manual.
- Bisong, E. (2019). Google colaboratory. In: Building machine learning and deep learning models on Google cloud platform. (Apress Berkeley CA., pp. 59–64). https:// doi.org/10.1007/978-1-4842-4470-8_7
- Talos, A. (2019). Autonomio Talos [Computer Software] hyperparameter optimization for tensorflow, Keras and Pytorch.
- Parashar, A., & Sonker, A. (2019). Application of hyperparameter optimized deep learning neural network for classification of air quality data. International Journal of Scientific & Technology Research, 8(11): 1435–1443.
- Ghasemi, E., Ataei, M., Shahriar, K., Sereshki, F., & Esmaeil, S.(2012). Assessment of roof fall risk during retreat mining in room and pillar coal mines. International Journal of Rock Mechanics and Mining Science, 54: 80–89. https:// doi.org/10.1016/j.ijrmms.2012.05.025
- Kumar, A., Kumar, D., Singh, A.K., Ram, S., Kumar, R., Gautam, A., Singh, R., & Singh, A.K. (2019). Roof sagging limit in an early warning system for safe coal pillar extraction. International Journal of Rock Mechanics and Mining Sciences, 123: 104–131. https://doi.org/10.1016/j. ijrmms.2019.104131
- Mark, C., & Michael, G. (2017). Preventing roof fall fatalities during pillar recovery: A ground control success story. International Journal of Mining Science and Technology, 27(1): 107–113. https://doi.org/10.1016/j.ijmst.2016.09.030
- Mark, C., & Molinda, G. (2007). Development and application of the Coal Mine Roof Rating (CMRR). Proceedings of the International Workshop on Rock Mass Classification in Underground Mining, 95–110.
- Palei, S.K., & Das, S.K. (2009). Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines:An approach. Safety Science, 47: 88–96. https:// doi.org/10.1016/j.ssci.2008.01.002
- Torres, J. (2018). First contact with Deep Learning, Practical introduction with Keras.
- Fan, C., Chen, M., Wang, X., Wang, J. & Huang, B. (2021). A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Sustainable Energy Systems and Policies, Frontiers in Energy Research, 9: 18. https://doi.org/10.3389/ fenrg.2021.652801
- Brownlee, J. (2020). Data preparation for machine learning:Data cleaning, feature selection and data transforms in Python.
- Godoy, D. (2018). Understanding binary cross-entropy/ log loss: A visual explanation.
- Kalantar, B., Pradhan, B., Naghibi, S.A., Motevalli, A., & Mansor, S. (2018). Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between Support Vector Machine (SVM), Logistic Regression (LR) and Artificial Neural Networks (ANN). Geomatics, Natural Hazards and Risk, 9(1): 49–69. https:// doi.org/10.1080/19475705.2017.1407368
- Goutte, C., & Gaussier, E.(2005). A probabilistic interpretation of precision, recall and F -score with implication for evaluation. Lecture Notes in Computer Science, 3408: 345– 359. https://doi.org/10.1007/978-3-540-31865-1_25
- Bradley, A.E. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7): 1145–1159. https://doi. org/10.1016/S0031-3203(96)00142-2