Date of Graduation
Statler College of Engineering and Mineral Resources
Mechanical and Aerospace Engineering
Machine automation and atmospheric monitoring are used by many industries to improve safety and productivity in the workplace. The underground Methane Watchdog System (MWS) is a multi-nodal network of sensors currently under development. The MWS aims to improve safety and productivity by introducing 10 compact sampling units designed to be easily integrated within the current roof support equipment of the mine. Each unit contains an array of sensors used to continuously monitor the environmental conditions which include methane concentration, temperature, pressure, and relative humidity. All MWS units report information back to a remote central processing hub (CPH) which collects nodal signals, converts them to useful engineering units, records data for historical analysis, and provides control capabilities.
The following work describes the methodology used to evaluate the MWS’s effectiveness within the laboratory setting and characterize the system’s response for improved performance. Reduced one-dimensional (1-D) modeling studies provided a useful structure to develop the longwall mining environment. Two-dimensional (2-D) computational fluid dynamic (CFD) models were also developed to evaluate timescales of mixing and formation of methane as a function of shearer position throughout a cutting sequence. From the 1-D studies, scenarios were constructed to generate temporal methane distributions that were the result of ventilation and production patterns. Model results were extracted from the proposed MWS sampling locations and used to demonstrate usefulness and effectiveness within the laboratory setting. The resulting outputs from the system were then used to develop a signal reconstruction technique to sharpen the responses and improve accuracy of real-time measurements. Commonly, system delay times result in a characteristic delayed and diffused output from the original input data.
The second-generation prototype of the MWS improved performance with the addition of a new methane sensor and signal reconstruction technique. When the developed reconstruction method was employed, measurement error for time aligned data was reduced by 78% on average. Moreover, system response times were reduced from 17 seconds to approximately 5 seconds. The reconstructed output captured all instances for a given scenario where methane emissions quickly rose above the regulatory threshold (1% CH4) from a baseline concentration of 0.4% CH4. A vast improvement from the original output, where no instances were detected. Original simulated concentrations, measured signals, and sharpened signals are provided to demonstrate the improvement.
Cappellini, Brian Philip, "Improving Real-time Methane Monitoring in Longwall Coal Mines Through System Response Characterization of a Multi-Nodal Methane Detection Network" (2021). Graduate Theses, Dissertations, and Problem Reports. 8333.