Acoustic emission potential for monitoring cutting and breakage characteristics of coal.

摘要

In the mining industry, the use of continuous mining, longwall mining, and shortwall mining has been growing in relation to conventional mining. The former methods utilize high speed rotating bits to remove the coal from the working face, and this generates a wide range of fragment sizes. Fine dust from underground mining operation has been blamed for several lung diseases such as Coal Workers' Pneumoconiosis (CWP) and Silicosis. In general, it is believed that the amount of fine coal dust generated during coal cutting is related to the cutting parameters, the bit condition, and the mechanical properties of the coal.; The purpose of this research was to investigate the character of the bit-coal interaction during coal cutting and its influence on the size and shape distributions of the generated dust, with the long term aim of improving dust control. It is appreciated that coal cutting mechanics are very complicated, and developing a mathematical or numerical model may be difficult, and impracticable for application to real mining situations. An innovative approach to investigate coal cutting and dust generation, namely; acoustic emission (AE) monitoring coupled with pattern recognition is evaluated for this purpose.; Two groups of coal cutting experiments, linear cutting and rotary cutting, have been carried out in the Rock Mechanics Laboratories at Penn State University and West Virginia University, respectively. Detailed particle size and shape distribution analyses were carried out on the cut material. A commercially available artificial intelligence package, ICEPAK (Intelligent Classifier Engineering Package) developed by Tektrend International Inc. has been utilized to provide a powerful and objective means for AE wave form analysis. AE signals associated with different coal cutting conditions were studied using the ICEPAK program.; The studies have provided new insights into the coal-cutting process, and have further promoted the feasibility of utilizing AE techniques for remote monitoring of coal cutting and dust generation. Additional research is required before the results of this research can be utilized in routine underground applications and recommendations for this research are included.