Abstract
Spontaneous combustion on industrial-scale stockpiles causes environmental problems and economic losses for the companies consuming large amounts of coal. In this study, an effective monitoring and prediction system based on internet was developed and implemented to prevent losses and environmental problems. The system was performed in a coal stockpile with 5 m width, 10 m length, 3 m height, and having 120 t of weight. The inner temperature data of the stockpile was recorded by 17 temperature sensors placed inside the stockpile at certain points. Additionally, the data relating to the air temperature, air humidity, atmospheric pressure, wind velocity, and wind direction that are the parameters affecting the coal stockpile were also recorded. The recorded values were analyzed with artificial neural network and Statistical modeling methods for prediction of spontaneous combustion. Real-time measurement values and model outputs were published with a web page on internet. The internet-based system can also provide real-time monitoring (combustion alarms, system status) and tele-controlling (Parameter adjusting, system control) through internet exclusively with a standard web browser without the need of any additional software.
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References
Akgun, F. (1994). Theoretical and experimental investigation of spontaneous coal combustion (pp. 1–142). PhD Thesis, Istanbul Technical University, Istanbul.
Canan, S., Özbay, Y., & Karlık, B. (1998). A method for removing low varying frequency trend from Ecg signal. In Proceedings of the 1998 2nd international conference biomedical engineering days (p. 161). Istanbul.
Chamberlain, E. A., Barrass, G., & Thirlaway, J. T. (1976). Gases evolved and possible reactions during low-temperature oxidation of coal. Fuel, 55(3), 217–222. doi:10.1016/0016-2361(76)90091-0.
Diamantidis, N. A., Karlis, D., & Giakoumakis, E. A. (2000). Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intelligence, 116(1–2), 1–16. doi:10.1016/S0004-3702(99)00094-6.
Francois, D., Rossi, F., Wertz, V., & Verleysen, M. (2007). Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing, 70(7–9), 1276–1288. doi:10.1016/j.neucom.2006.11.019.
Gill, F., & Browning, E. (1971). Spontaneous combustion in coal mines. Colliery Guardian, 219, 79–85.
Haykin, S. (1994). Neural networks: A comprehensive foundation. New York: Macmillan College Publishing.
Konuk, A., & Onder, S. (1999). Statistics for mining engineers (pp. 1–155). Eskisehir: Osmangazi University Eng. Arch. Faculty, Mining Eng. Dept.
Maren, A., Harston, C., & Pap, R. (1990). Handbook of neural computing applications. London: Academic.
Ozdeniz, A. H. (2003). Investigation of spontaneous combustion event in coal Stockpiles-Western Lignite corporation case (p. 1–185). PhD Thesis, Selcuk University, Konya.
Ozdeniz, A. H. (2009). Statistical modeling of spontaneous combustion in industrial-scale coal stockpiles. Energy sources, Part A. (in press).
Ozdeniz, A. H., Ozbay, Y., Yilmaz, N., & Sensogut, C. (2008). Monitoring and ANN modeling of coal stockpile behavior under different atmospheric conditions. Energy Sources, Part A-Recovery Utilization and Environmental Effects,, 30(6), 494–507.
Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing. Cambridge: MIT.
Sensogut, C., & Ozdeniz, A. H. (2005). Statistical modeling of stockpile behavior under different atmospheric conditions - Western Lignite Corporation (WLC) case. Fuel, 84, 1858–1863. doi:10.1016/j.fuel.2005.03.027.
Song, G. M., Song, A. G., & Huang, W. Y. (2005). Distributed measurement system based on networked smart sensors with standardized interfaces. Sensors and Actuators. A, Physical, 120(1), 147–153. doi:10.1016/j.sna.2004.11.011.
Unver, B., & Ozozen, A. (1998). Models related with the time period of the spontaneous combustion occurring in coal stockpiles and precautions to be taken. Mining Journal of Turkey, Ankara, 37, 29–40.
Yang, S. H., Chen, X., & Alty, J. L. (2003). Design issues and implementation of internet-based process control systems. Control Engineering Practice, 11(6), 709–720. doi:10.1016/S0967-0661(02)00181-8.
Yilmaz, N., Sagiroglu, S., & Bayrak, M. (2006). General aimed web based mobile robot. SUNAR Journal of The Faculty of Engineering and Architecture of Gazi University, 21(4), 745–752.
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Yilmaz, N., Ozdeniz, A.H. Internet-based monitoring and prediction system of coal stockpile behaviors under atmospheric conditions. Environ Monit Assess 162, 103–112 (2010). https://doi.org/10.1007/s10661-009-0779-y
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DOI: https://doi.org/10.1007/s10661-009-0779-y